Part 1 - Analysis & Dataset Cleaning

1 Loads & Install Packages

# installation of packages in case the user has not installed yet
if (!require("nnet")) install.packages("nnet")

## Caricamento del pacchetto richiesto: nnet

if (!require("MASS")) install.packages("MASS")

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if (!require("e1071")) install.packages("e1071")

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if (!require("class")) install.packages("class")

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if (!require("leaps")) install.packages("leaps")

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if (!require("glmnet")) install.packages("glmnet")

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if (!require("car")) install.packages("car")

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if (!require("caTools")) install.packages("caTools")

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if (!require("pROC")) install.packages("pRoc")

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if (!require("summarytools")) install.packages("summarytools")

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if (!require("dplyr")) install.packages("dplyr")

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if (!require("ggplot2")) install.packages("ggplot2")

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if (!require("tidyverse")) install.packages("tidyverse")

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if (!require("lubridate")) install.packages("lubridate")
if (!require("mapview")) install.packages("mapview")

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if (!require("leafpop")) install.packages("leafpop")

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if (!require("sf")) install.packages("sf")

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if (!require("broom")) install.packages("broom")

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# laoding of packages
library(summarytools)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(mapview)
library(sf)
library(geojsonio)
library(leaflet) 
library(broom)
library(plotly)

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library(gridExtra)
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2 Dataset description

The Fire Incident Dispatch Data file contains data that is generated by the Starfire Computer Aided Dispatch System. The data spans from the time the incident is created in the system to the time the incident is closed in the system. It covers information about the incident as it relates to the assignment of resources and the Fire Department’s response to the emergency. To protect personal identifying information in accordance with the Health Insurance Portability and Accountability Act (HIPAA), specific locations of incidents are not included and have been aggregated to a higher level of detail.

In this analysis we have restricted the number of observations to the last 50.000, from 5th of September to 30th of the same month.

The dataset is formed by the following columns:

1. STARFIRE_INCIDENT_ID: An incident identifier comprising the 5 character julian date, 4 character alarm box number, 2 character number of incidents at the box so far for the day, 1 character borough code , 4 character sequence number.

2. INCIDENT_DATETIME: The date and time of the incident.

3. ALARM_BOX_BOROUGH: The borough of the alarm box.

4. ALARM_BOX_LOCATION: The location of the alarm box.

5. ALARM_BOX: The alarm box number.

6. INCIDENT_BOROUGH: The borough of the incident.

7. ZIPCODE: The zip code of the incident.

8. POLICEPRECINCT: The police precinct of the incident.

9. CITYCOUNCILDISTRICT: The city council district.

10. COMMUNITYDISTRICT: The community district.

11. COMMUNITYSCHOOLDISTRICT: The community school district.

12. CONGRESSIONALDISTRICT: The congressional district.

13. ALARM_SOURCE_DESCRIPTION_TX: The description of the alarm source.

14. ALARM_LEVEL_INDEX_DESCRIPTION: The alarm level index.

15. HIGHEST_ALARM_LEVEL: The highest alarm level.

16. INCIDENT_CLASSIFICATION: The incident classification.

17. INCIDENT_CLASSIFICATION_GROUP: The incident classification roll up group.

18. FIRST_ASSIGNMENT_DATETIME: The date and time of the first unit assignment.

19. FIRST_ACTIVATION_DATETIME: The date and time of the first unit acknowledgement of the assignment.

20. FIRST_ON_SCENE_DATETIME: The date and time of the first unit at the scene of the incident.

21. INCIDENT_CLOSE_DATETIME: The date and time that the incident was closed in the dispatch system.

22. VALID_DISPATCH_RSPNS_TIME_INDC: Indicates that the components comprising the generation of the DISPATCH_RESPONSE_SECONDS_QY are valid.

23. DISPATCH_RESPONSE_SECONDS_QY: The elapsed time in seconds between the INCIDENT_DATETIME and the FIRST_ASSIGNMENT_DATETIME.

24. VALID_INCIDENT_RSPNS_TIME_INDC: Indicates that the components comprising the generation of the INCIDENT_RESPONSE_SECONDS_QY are valid.

25. INCIDENT_RESPONSE_SECONDS_QY: The elapsed time in seconds between the INCIDENT_DATETIME and the FIRST_ON_SCENE_DATETIME.

26. INCIDENT_TRAVEL_TM_SECONDS_QY: The elapsed time in seconds between the FIRST_ASSIGNMENT_DATETIME and the FIRST_ON_SCENE_DATETIME.

27. ENGINES_ASSIGNED_QUANTITY: The number of engine units assigned to the incident.

28. LADDERS_ASSIGNED_QUANTITY: The number of ladder units assigned to the incident.

29. OTHER_UNITS_ASSIGNED_QUANTITY: The number of units that are not engines or ladders that were assigned to the incident.

2.1 Analysis Description

We will try create two different analysis.

1. The aim to predict the INCIDENT_RESPONSE_SECONDS_QY which is the time difference between the FIRST_ON_SCENE_DATETIME and INCIDENT_DATETIME.
2. The focus is to predict the EMERGENCY_TIME which is the time difference between the FIRST_ON_SCENE_DATETIME and INCIDENT_CLOSE_DATETIME.

In both analysis tried to use a linear regression model, however we will see that the assumption for applying the linear regression are not meet, thus we will simplify our project moving into classification, dividing in two or more ranges the two responses. In addition of this we will perform data exploration and cleaning, studying the presence or not of pattern of NA values and invalid values.

3 Data Exlporation and Cleaning

The first step is always to read the dataset, plot the first 5 observations and see its whole dimension.

fire_data <- read.csv("datasets/Fire_Incident_Dispatch_Data_last_50k.csv")

head(fire_data)

##    STARFIRE_INCIDENT_ID      INCIDENT_DATETIME ALARM_BOX_BOROUGH
## 1 230905-B1937-001-0567 09/05/2023 02:19:04 PM          BROOKLYN
## 2 230905-B3923-002-0568 09/05/2023 02:19:36 PM          BROOKLYN
## 3 230905-X8897-003-0480 09/05/2023 02:19:43 PM             BRONX
## 4 230905-X3466-001-0481 09/05/2023 02:21:00 PM             BRONX
## 5 230905-B2448-001-0570 09/05/2023 02:21:26 PM          BROOKLYN
## 6 230905-B2448-002-0571 09/05/2023 02:22:35 PM          BROOKLYN
##   ALARM_BOX_NUMBER                    ALARM_BOX_LOCATION INCIDENT_BOROUGH
## 1             1937                AUTUMN AVE & FULTON ST         BROOKLYN
## 2             3923          N/S EASTERN PWAY & UTICA AVE         BROOKLYN
## 3             8897 CROSS BX EXPY- DEEGAN EX TO JEROME AV            BRONX
## 4             3466                  ADEE AVE & BX PARK E            BRONX
## 5             2448             GLENWOOD RD & BEDFORD AVE         BROOKLYN
## 6             2448             GLENWOOD RD & BEDFORD AVE         BROOKLYN
##   ZIPCODE POLICEPRECINCT CITYCOUNCILDISTRICT COMMUNITYDISTRICT
## 1   11208             75                  37               305
## 2   11213             71                  35               309
## 3      NA             NA                  NA                NA
## 4   10467             49                  12               211
## 5   11210             70                  45               314
## 6   11210             70                  45               314
##   COMMUNITYSCHOOLDISTRICT CONGRESSIONALDISTRICT ALARM_SOURCE_DESCRIPTION_TX
## 1                      19                     7                         EMS
## 2                      17                     9                     CLASS-3
## 3                      NA                    NA                     EMS-911
## 4                      11                    15                         EMS
## 5                      22                     9                         EMS
## 6                      22                     9                         EMS
##   ALARM_LEVEL_INDEX_DESCRIPTION HIGHEST_ALARM_LEVEL
## 1                 Initial Alarm         First Alarm
## 2                 Initial Alarm         First Alarm
## 3                 Initial Alarm         First Alarm
## 4                DEFAULT RECORD         First Alarm
## 5                DEFAULT RECORD         First Alarm
## 6                DEFAULT RECORD         First Alarm
##                  INCIDENT_CLASSIFICATION INCIDENT_CLASSIFICATION_GROUP
## 1 Medical - No PT Contact EMS is Onscene           Medical Emergencies
## 2                          Hospital Fire              Structural Fires
## 3               Vehicle Accident - Other        NonMedical Emergencies
## 4               Medical - EMS Link 10-91           Medical Emergencies
## 5               Medical - EMS Link 10-91           Medical Emergencies
## 6               Medical - EMS Link 10-91           Medical Emergencies
##   DISPATCH_RESPONSE_SECONDS_QY FIRST_ASSIGNMENT_DATETIME
## 1                            7    09/05/2023 02:19:12 PM
## 2                           95    09/05/2023 02:21:11 PM
## 3                           41    09/05/2023 02:20:25 PM
## 4                          298    09/05/2023 02:25:59 PM
## 5                           25    09/05/2023 02:21:52 PM
## 6                          350    09/05/2023 02:28:25 PM
##   FIRST_ACTIVATION_DATETIME FIRST_ON_SCENE_DATETIME INCIDENT_CLOSE_DATETIME
## 1    09/05/2023 02:19:26 PM  09/05/2023 02:25:23 PM  09/05/2023 03:03:15 PM
## 2    09/05/2023 02:21:33 PM  09/05/2023 02:23:21 PM  09/05/2023 02:34:18 PM
## 3    09/05/2023 02:20:35 PM  09/05/2023 02:26:22 PM  09/05/2023 04:13:32 PM
## 4    09/05/2023 02:26:04 PM                          09/05/2023 02:34:23 PM
## 5    09/05/2023 02:22:08 PM                          09/05/2023 02:28:07 PM
## 6                                                    09/05/2023 02:29:09 PM
##   VALID_DISPATCH_RSPNS_TIME_INDC VALID_INCIDENT_RSPNS_TIME_INDC
## 1                              N                              Y
## 2                              N                              Y
## 3                              N                              Y
## 4                              N                              N
## 5                              N                              N
## 6                              N                              N
##   INCIDENT_RESPONSE_SECONDS_QY INCIDENT_TRAVEL_TM_SECONDS_QY
## 1                          378                           371
## 2                          224                           129
## 3                          398                           357
## 4                           NA                            NA
## 5                           NA                            NA
## 6                           NA                            NA
##   ENGINES_ASSIGNED_QUANTITY LADDERS_ASSIGNED_QUANTITY
## 1                         1                         0
## 2                         3                         2
## 3                         2                         3
## 4                         1                         0
## 5                         1                         0
## 6                         1                         0
##   OTHER_UNITS_ASSIGNED_QUANTITY
## 1                             0
## 2                             1
## 3                             1
## 4                             0
## 5                             0
## 6                             0

dim(fire_data)

## [1] 50000    29

We ese dfSummary from summarytool in order to have a complete and clear summary of the dataset.

print(dfSummary(fire_data, 
                plain.ascii  = FALSE, 
                style        = "multiline", 
                headings     = FALSE,
                graph.magnif = 0.8, 
                valid.col    = FALSE),
                method = 'render')

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	STARFIRE_INCIDENT_ID [character]	1. 230905-B0042-001-1051 2. 230905-B0053-001-0760 3. 230905-B0053-002-0910 4. 230905-B0081-001-1137 5. 230905-B0106-002-0632 6. 230905-B0132-001-0713 7. 230905-B0147-001-0967 8. 230905-B0160-001-1125 9. 230905-B0163-001-1026 10. 230905-B0165-001-0778 [ 49990 others ]	1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 49990 ( 100.0% )		0 (0.0%)
2	INCIDENT_DATETIME [character]	1. 09/07/2023 03:53:19 PM 2. 09/11/2023 09:44:33 AM 3. 09/13/2023 12:09:35 AM 4. 09/29/2023 09:44:26 AM 5. 09/05/2023 03:30:51 PM 6. 09/05/2023 03:37:48 PM 7. 09/05/2023 03:53:11 PM 8. 09/05/2023 04:01:29 PM 9. 09/05/2023 04:32:57 PM 10. 09/05/2023 04:59:57 PM [ 49364 others ]	3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 49976 ( 100.0% )		0 (0.0%)
3	ALARM_BOX_BOROUGH [character]	1. BRONX 2. BROOKLYN 3. MANHATTAN 4. QUEENS 5. RICHMOND / STATEN ISLAND	10973 ( 21.9% ) 13980 ( 28.0% ) 12890 ( 25.8% ) 9879 ( 19.8% ) 2278 ( 4.6% )		0 (0.0%)
4	ALARM_BOX_NUMBER [integer]	Mean (sd) : 2930.3 (2446.5) min ≤ med ≤ max: 10 ≤ 2275 ≤ 9933 IQR (CV) : 2772 (0.8)	7411 distinct values		0 (0.0%)
5	ALARM_BOX_LOCATION [character]	1. 8 AVE & W 155 ST 2. 10 RICHMAN PLZ/SEDGWICK A 3. AMSTERDAM AVE & LA SALLE 4. 3 AVE & E 143 ST 5. WASHINGTON AVE & E 170 ST 6. FDR DR & E 6 ST 7. CONCOURSE VILLAGE E & E 1 8. PARK AVE & E 158 ST 9. UNION TPK & WINCHESTER BL 10. 8 AVE & W 33 ST [ 12203 others ]	85 ( 0.2% ) 75 ( 0.1% ) 50 ( 0.1% ) 48 ( 0.1% ) 48 ( 0.1% ) 45 ( 0.1% ) 44 ( 0.1% ) 40 ( 0.1% ) 40 ( 0.1% ) 39 ( 0.1% ) 49486 ( 99.0% )		0 (0.0%)
6	INCIDENT_BOROUGH [character]	1. BRONX 2. BROOKLYN 3. MANHATTAN 4. QUEENS 5. RICHMOND / STATEN ISLAND	10973 ( 21.9% ) 13980 ( 28.0% ) 12890 ( 25.8% ) 9879 ( 19.8% ) 2278 ( 4.6% )		0 (0.0%)
7	ZIPCODE [integer]	Mean (sd) : 10737.9 (551.8) min ≤ med ≤ max: 10000 ≤ 10472 ≤ 11697 IQR (CV) : 1098 (0.1)	217 distinct values		3181 (6.4%)
8	POLICEPRECINCT [integer]	Mean (sd) : 62.3 (34.8) min ≤ med ≤ max: 1 ≤ 61 ≤ 123 IQR (CV) : 56 (0.6)	77 distinct values		3180 (6.4%)
9	CITYCOUNCILDISTRICT [integer]	Mean (sd) : 23.1 (15.1) min ≤ med ≤ max: 1 ≤ 21 ≤ 51 IQR (CV) : 27 (0.7)	51 distinct values		3180 (6.4%)
10	COMMUNITYDISTRICT [integer]	Mean (sd) : 262.9 (119.4) min ≤ med ≤ max: 101 ≤ 302 ≤ 595 IQR (CV) : 206 (0.5)	70 distinct values		3180 (6.4%)
11	COMMUNITYSCHOOLDISTRICT [integer]	Mean (sd) : 14.8 (9.7) min ≤ med ≤ max: 1 ≤ 13 ≤ 32 IQR (CV) : 18 (0.7)	32 distinct values		3182 (6.4%)
12	CONGRESSIONALDISTRICT [integer]	Mean (sd) : 10.4 (3.3) min ≤ med ≤ max: 3 ≤ 11 ≤ 16 IQR (CV) : 5 (0.3)	13 distinct values		3180 (6.4%)
13	ALARM_SOURCE_DESCRIPTION_TX [character]	1. 911 2. 911TEXT 3. BARS 4. CLASS-3 5. EMS 6. EMS-911 7. ERS 8. ERS-NC 9. PHONE 10. SOL 11. VERBAL	302 ( 0.6% ) 14 ( 0.0% ) 1 ( 0.0% ) 5025 ( 10.1% ) 17178 ( 34.4% ) 10520 ( 21.0% ) 777 ( 1.6% ) 1 ( 0.0% ) 15146 ( 30.3% ) 5 ( 0.0% ) 1031 ( 2.1% )		0 (0.0%)
14	ALARM_LEVEL_INDEX_DESCRIPTION [character]	1. 10-75 Signal (Request for 2. 10-76 & 10-77 Signal (Not 3. 7-5 (All Hands Alarm) 4. DEFAULT RECORD 5. Initial Alarm 6. Second Alarm 7. Third Alarm	13 ( 0.0% ) 3 ( 0.0% ) 100 ( 0.2% ) 17313 ( 34.6% ) 32562 ( 65.1% ) 8 ( 0.0% ) 1 ( 0.0% )		0 (0.0%)
15	HIGHEST_ALARM_LEVEL [character]	1. All Hands Working 2. First Alarm 3. Second Alarm 4. Third Alarm	100 ( 0.2% ) 49891 ( 99.8% ) 8 ( 0.0% ) 1 ( 0.0% )		0 (0.0%)
16	INCIDENT_CLASSIFICATION [character]	1. Medical - EMS Link 10-91 2. Medical - PD Link 10-91 3. Medical - Breathing / Ill 4. Medical - No PT Contact E 5. Assist Civilian - Non-Med 6. Alarm System - Unnecessar 7. Elevator Emergency - Occu 8. Vehicle Accident - Other 9. Utility Emergency - Gas 10. Odor - Other Than Smoke [ 57 others ]	9509 ( 19.0% ) 5741 ( 11.5% ) 5453 ( 10.9% ) 5013 ( 10.0% ) 4140 ( 8.3% ) 2845 ( 5.7% ) 1954 ( 3.9% ) 1543 ( 3.1% ) 1359 ( 2.7% ) 1337 ( 2.7% ) 11106 ( 22.2% )		0 (0.0%)
17	INCIDENT_CLASSIFICATION_GROUP [character]	1. Medical Emergencies 2. Medical MFAs 3. NonMedical Emergencies 4. NonMedical MFAs 5. NonStructural Fires 6. Structural Fires	26824 ( 53.6% ) 208 ( 0.4% ) 19072 ( 38.1% ) 1680 ( 3.4% ) 703 ( 1.4% ) 1513 ( 3.0% )		0 (0.0%)
18	DISPATCH_RESPONSE_SECONDS_QY [integer]	Mean (sd) : 40 (133.1) min ≤ med ≤ max: 2 ≤ 19 ≤ 9023 IQR (CV) : 33 (3.3)	841 distinct values		0 (0.0%)
19	FIRST_ASSIGNMENT_DATETIME [character]	1. 09/06/2023 01:40:49 PM 2. 09/08/2023 02:43:53 PM 3. 09/20/2023 01:09:29 PM 4. 09/27/2023 02:04:41 PM 5. 09/05/2023 02:34:37 PM 6. 09/05/2023 03:38:43 PM 7. 09/05/2023 03:48:54 PM 8. 09/05/2023 03:56:07 PM 9. 09/05/2023 05:01:22 PM 10. 09/05/2023 07:13:08 PM [ 49499 others ]	3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 49976 ( 100.0% )		0 (0.0%)
20	FIRST_ACTIVATION_DATETIME [character]	1. (Empty string) 2. 09/22/2023 02:07:47 PM 3. 09/07/2023 06:59:12 PM 4. 09/10/2023 06:28:10 PM 5. 09/17/2023 07:27:03 PM 6. 09/23/2023 08:01:29 PM 7. 09/25/2023 10:17:28 AM 8. 09/29/2023 08:16:05 AM 9. 09/05/2023 02:47:25 PM 10. 09/05/2023 03:00:12 PM [ 49196 others ]	139 ( 0.3% ) 4 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 49835 ( 99.7% )		0 (0.0%)
21	FIRST_ON_SCENE_DATETIME [character]	1. (Empty string) 2. 09/30/2023 04:01:43 PM 3. 09/05/2023 03:17:20 PM 4. 09/05/2023 03:27:35 PM 5. 09/05/2023 04:37:22 PM 6. 09/05/2023 04:38:47 PM 7. 09/05/2023 04:39:30 PM 8. 09/05/2023 05:44:27 PM 9. 09/05/2023 05:55:56 PM 10. 09/05/2023 08:59:49 PM [ 35543 others ]	14112 ( 28.2% ) 3 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 35869 ( 71.7% )		0 (0.0%)
22	INCIDENT_CLOSE_DATETIME [character]	1. 09/05/2023 06:13:06 PM 2. 09/10/2023 02:16:37 PM 3. 09/24/2023 04:10:06 PM 4. 09/25/2023 12:20:57 AM 5. 09/27/2023 04:38:25 PM 6. 09/29/2023 10:42:38 AM 7. 09/30/2023 06:06:40 PM 8. 09/05/2023 03:25:13 PM 9. 09/05/2023 04:08:06 PM 10. 09/05/2023 05:08:09 PM [ 49399 others ]	3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 3 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 2 ( 0.0% ) 49973 ( 99.9% )		0 (0.0%)
23	VALID_DISPATCH_RSPNS_TIME_INDC [character]	1. N	50000 ( 100.0% )		0 (0.0%)
24	VALID_INCIDENT_RSPNS_TIME_INDC [character]	1. N 2. Y	17036 ( 34.1% ) 32964 ( 65.9% )		0 (0.0%)
25	INCIDENT_RESPONSE_SECONDS_QY [integer]	Mean (sd) : 380.7 (233.2) min ≤ med ≤ max: 18 ≤ 334 ≤ 7130 IQR (CV) : 161 (0.6)	1496 distinct values		14112 (28.2%)
26	INCIDENT_TRAVEL_TM_SECONDS_QY [integer]	Mean (sd) : 340.5 (208.6) min ≤ med ≤ max: 0 ≤ 301 ≤ 7122 IQR (CV) : 159 (0.6)	1382 distinct values		14112 (28.2%)
27	ENGINES_ASSIGNED_QUANTITY [integer]	Mean (sd) : 1.1 (0.8) min ≤ med ≤ max: 0 ≤ 1 ≤ 19 IQR (CV) : 0 (0.7)	15 distinct values		62 (0.1%)
28	LADDERS_ASSIGNED_QUANTITY [integer]	Mean (sd) : 0.6 (0.8) min ≤ med ≤ max: 0 ≤ 0 ≤ 15 IQR (CV) : 1 (1.4)	12 distinct values		62 (0.1%)
29	OTHER_UNITS_ASSIGNED_QUANTITY [integer]	Mean (sd) : 0.3 (0.8) min ≤ med ≤ max: 0 ≤ 0 ≤ 32 IQR (CV) : 0 (2.8)	23 distinct values		62 (0.1%)

Generated by summarytools 1.0.1 (R version 4.3.2)
2024-01-18

Now we decide to rename all the columns in order to be of smaller length once we plots some charts.

fire_data <- fire_data %>%
  rename(id = STARFIRE_INCIDENT_ID, datetime = INCIDENT_DATETIME, al_borough = ALARM_BOX_BOROUGH,
    al_number = ALARM_BOX_NUMBER,al_location = ALARM_BOX_LOCATION, inc_borough = INCIDENT_BOROUGH,
    zipcode = ZIPCODE, pol_prec = POLICEPRECINCT, city_con_dist = CITYCOUNCILDISTRICT,
    commu_dist = COMMUNITYDISTRICT, commu_sc_dist = COMMUNITYSCHOOLDISTRICT,
    cong_dist = CONGRESSIONALDISTRICT, al_source_desc = ALARM_SOURCE_DESCRIPTION_TX,
    al_index_desc = ALARM_LEVEL_INDEX_DESCRIPTION, highest_al_level = HIGHEST_ALARM_LEVEL,
    inc_class = INCIDENT_CLASSIFICATION, inc_class_group = INCIDENT_CLASSIFICATION_GROUP,
    first_ass_datetime = FIRST_ASSIGNMENT_DATETIME, first_act_datetime = FIRST_ACTIVATION_DATETIME,
    first_onscene_datetime = FIRST_ON_SCENE_DATETIME, inc_close_datetime = INCIDENT_CLOSE_DATETIME, 
                   
    disp_resp_sec_qy = DISPATCH_RESPONSE_SECONDS_QY, disp_resp_sec_indc = VALID_DISPATCH_RSPNS_TIME_INDC,
    inc_resp_sec_qy = INCIDENT_RESPONSE_SECONDS_QY, inc_resp_sec_indc = VALID_INCIDENT_RSPNS_TIME_INDC,
                   
    inc_travel_sec_qy = INCIDENT_TRAVEL_TM_SECONDS_QY, 
                   
    engines_assigned = ENGINES_ASSIGNED_QUANTITY,
    ladders_assigned = LADDERS_ASSIGNED_QUANTITY, others_units_assigned = OTHER_UNITS_ASSIGNED_QUANTITY)

As we can see from the summary there are lots of work to do, in fact initially we identify the following problem:

1. Many NA values
2. Lots of predictors are characters and not factor
3. Many future factorial predictors have values that occurs very small time, this suggest that we can inglobe those in a bigger category
4. The differences of datatime have huge variation, we can think on scaling the relative values
5. Possible duplicate columns

We start by converting the non factorial columns into factorial one. Then we perform accurate data exploration and cleaning in each macro columns set.

# set factorial
fire_data$inc_borough <- as.factor(fire_data$inc_borough)
fire_data$al_borough <- as.factor(fire_data$al_borough)
fire_data$al_source_desc <- as.factor(fire_data$al_source_desc)
fire_data$al_index_desc <- as.factor(fire_data$al_index_desc)
fire_data$highest_al_level <- as.factor(fire_data$highest_al_level)
fire_data$cong_dist <- as.factor(fire_data$cong_dist)

fire_data$disp_resp_sec_indc <- as.factor(fire_data$disp_resp_sec_indc)
levels(fire_data$disp_resp_sec_indc)<- c("N", "Y")

fire_data$inc_resp_sec_indc <- as.factor(fire_data$inc_resp_sec_indc)
levels(fire_data$inc_resp_sec_indc)<- c("N", "Y")

fire_data$inc_class_group <- as.factor(fire_data$inc_class_group)
fire_data$inc_class <- as.factor(fire_data$inc_class)

3.1 Process Temporal Data

For the temporal data processing we employ a little of feature engineering. Indeed we decide to add the following predictors:

1. day_type: a factorial predictor to indicate if the incident day is a week day or not
2. time_of_day: a factorial predictor that indicates the range of time whenever the incident happens, so Night if the hour is between 0 and 6, Morning if the hour is between 6 and 12, Afternoon if the hour is between 12 and 18 and Evening if the hour is between 18 and 24.
3. emergency_min_qy: which represents the difference between the inc_close_datetime and the first_onscene_datetime.
4. working_hour: indicates if the incident happened between 8AM and 7PM, so the classic working hour.

Moreover since we are dealing with datetime we also check if the time differences inc_resp_sec_qy, inc_travel_sec_qy and disp_resp_sec_qy are actually corrects, if not we replace them with the correct one, by performning the subtraction of the relative datetime (see dataset description).

Now we note that the maximum level of the time differences is very high to be considered as seconds so we decided to scale all the time differences in minutes.

summary(fire_data %>% select(inc_resp_sec_qy, inc_travel_sec_qy, disp_resp_sec_qy))

##  inc_resp_sec_qy  inc_travel_sec_qy disp_resp_sec_qy 
##  Min.   :  18.0   Min.   :   0.0    Min.   :   2.00  
##  1st Qu.: 265.0   1st Qu.: 233.0    1st Qu.:   7.00  
##  Median : 334.0   Median : 301.0    Median :  19.00  
##  Mean   : 380.7   Mean   : 340.5    Mean   :  39.96  
##  3rd Qu.: 426.0   3rd Qu.: 392.0    3rd Qu.:  40.00  
##  Max.   :7130.0   Max.   :7122.0    Max.   :9023.00  
##  NA's   :14112    NA's   :14112

# scaling
fire_data$inc_resp_sec_qy <- fire_data$inc_resp_sec_qy / 60
fire_data$inc_travel_sec_qy <- fire_data$inc_travel_sec_qy / 60
fire_data$disp_resp_sec_qy <- fire_data$disp_resp_sec_qy / 60 

# renaming both quantity and indicator predictors for the two datetime 
fire_data <- fire_data %>% rename(inc_resp_min_qy = inc_resp_sec_qy, inc_travel_min_qy = inc_travel_sec_qy, disp_resp_min_qy = disp_resp_sec_qy, # quantity
                                  inc_resp_min_indc = inc_resp_sec_indc, disp_resp_min_indc = disp_resp_sec_indc) # indicator

Perform the datetime feature engineering that we have discussed before.

# Process datetime column
fire_data$datetime <- mdy_hms(fire_data$datetime)
fire_data$first_ass_datetime <- mdy_hms(fire_data$first_ass_datetime)
fire_data$first_act_datetime <- mdy_hms(fire_data$first_act_datetime)
fire_data$first_onscene_datetime <- mdy_hms(fire_data$first_onscene_datetime)
fire_data$inc_close_datetime <- mdy_hms(fire_data$inc_close_datetime)


# checking if the differences are well computed if not change with the correct one

if (!identical(fire_data$inc_resp_min_qy, as.numeric(difftime(fire_data$first_onscene_datetime, fire_data$datetime, units="mins")))){
  fire_data$inc_resp_min_qy <- as.numeric(difftime(fire_data$first_onscene_datetime, fire_data$datetime, units="mins"))
}

if (!identical(fire_data$inc_travel_min_qy, as.numeric(difftime(fire_data$first_onscene_datetime, fire_data$first_ass_datetime, units="mins")))){
  fire_data$inc_travel_min_qy <- as.numeric(difftime(fire_data$first_onscene_datetime, fire_data$first_ass_datetime, units="mins"))
}

if (!identical(fire_data$disp_resp_min_qy, as.numeric(difftime(fire_data$first_ass_datetime, fire_data$datetime, units="mins")))){
  fire_data$disp_resp_min_qy <- as.numeric(difftime(fire_data$first_ass_datetime, fire_data$datetime, units="mins"))
}

# creating emergency_min_qy which describe the time taken by the firefighter to close the emergency after have been arrived to the location 
fire_data$emergency_min_qy <- as.numeric(difftime(fire_data$inc_close_datetime, fire_data$first_onscene_datetime, units="mins"))

# creating day_type
fire_data$day_type <- as.factor(ifelse(weekdays(fire_data$datetime) %in% c("sabato", "domenica"), "Weekend", "Weekday"))

# creating working_hour
fire_data$working_hour <- as.factor(ifelse(hour(fire_data$datetime) >= 19 | hour(fire_data$datetime) < 8, FALSE, TRUE))
  
# creating time_of_day
fire_data$time_of_day <- cut(
    hour(fire_data$datetime),
    breaks = c(0, 6, 12, 18, 24),
    labels = c("Night", "Morning", "Afternoon", "Evening"),
    include.lowest = TRUE,
    right = TRUE
)

# delete the datetime
fire_data$datetime <- NULL

Check the distribution of time_of_day

table(fire_data$time_of_day)

## 
##     Night   Morning Afternoon   Evening 
##      8521     13270     16499     11710

ggplot(data=fire_data %>% 
          group_by(time_of_day) %>%
          summarise(incident_number = n()), 
        aes(x=time_of_day, y=incident_number)) + 
      geom_bar(stat="identity", position=position_dodge()) +
      geom_text(aes(label=incident_number), vjust=1.6, color="white", position = position_dodge(0.9), size=3.5) +
      labs(title = "Time of the Day - Incident Count", x = "Time of the Day", y = "Incident Count")

From this we can see that the higher number of fire incident is registered from 12 PM to 18 PM, whereas the lower number of fire incident happened from the 00 AM to 06 AM.

Check the distribution of day_type

table(fire_data$day_type)

## 
## Weekday Weekend 
##   36482   13518

day_type_table <- table(fire_data$day_type)
day_type_table[1] <- day_type_table[1] / 5
day_type_table[2] <- day_type_table[2] / 2
day_type_table

## 
## Weekday Weekend 
##  7296.4  6759.0

And in proportion we can see that on average there is an higher number of fire incident on the week day respect to the week end days.

Check the distribution of working_hour

table(fire_data$working_hour)

## 
## FALSE  TRUE 
## 21859 28141

table(fire_data$working_hour) / dim(fire_data)[1]

## 
##   FALSE    TRUE 
## 0.43718 0.56282

It seems like that there are slightly more incident during the working hour.

3.2 Column Value Renaming

3.2.1 inc_borough and al_borough

Rename the factor levels for the inc_borough and al_borough.

fire_data <- fire_data %>% mutate(inc_borough = recode_factor(
      inc_borough, "BRONX" = "Bronx", "BROOKLYN" = "Brooklyn", "MANHATTAN" = "Manhattan",
      "QUEENS" = "Queens", "RICHMOND / STATEN ISLAND" = "Staten Island"),
                  
      al_borough = recode_factor(
      al_borough, "BRONX" = "Bronx", "BROOKLYN" = "Brooklyn", "MANHATTAN" = "Manhattan",
      "QUEENS" = "Queens", "RICHMOND / STATEN ISLAND" = "Staten Island"))

Regarding the Spatial Data we decide to keep the borough and also the congressional district since are the two predictors that have the least number of categories. Further in the section of data visualization we have a look on an interactive map of some relevant information for both aspects.

3.2.2 inc_class_group

fire_data <- fire_data %>% mutate(inc_class_group = recode_factor(
      inc_class_group, "Medical Emergencies" = "M_E", "NonMedical Emergencies" = "NonM_E", "Medical MFAs" = "M_MFAs",
      "NonMedical MFAs" = "NonMedical MFAs", "Structural_Fires" = "S_F", "NonStructural Fires" = "NonS_F"))

3.3 Merging Factors

At this point we merge some possible value from factorial predictors to make their value space of smaller size.

3.3.1 Highest Alarm Level

Here we merge the following factorial values of highest_al_level: All Hands Working, Second Alarm and Third Alarm into NonFirst Alarm. But before doing so let’s see the actual distribution of values.

table(fire_data$highest_al_level)

## 
## All Hands Working       First Alarm      Second Alarm       Third Alarm 
##               100             49891                 8                 1

As we can see the majority of the observations are of the type First Alarm, whereas the other observation reach 109 observation, thus we concatenate the less category values into a single one called NonFirst Alarm

fire_data$highest_alarm_lev_new <- fire_data$highest_al_level
levels(fire_data$highest_alarm_lev_new) <- list(
  "First Alarm" = "First Alarm", 
  "NonFirst Alarm" = c("All Hands Working", "Second Alarm", "Third Alarm")
)

ctable(fire_data$highest_al_level, fire_data$highest_alarm_lev_new, prop = 'n', totals = FALSE, headings = FALSE)

## 
## ------------------- ----------------------- ------------- ----------------
##                       highest_alarm_lev_new   First Alarm   NonFirst Alarm
##    highest_al_level                                                       
##   All Hands Working                                     0              100
##         First Alarm                                 49891                0
##        Second Alarm                                     0                8
##         Third Alarm                                     0                1
## ------------------- ----------------------- ------------- ----------------

fire_data$highest_al_level <- fire_data$highest_alarm_lev_new
fire_data$highest_alarm_lev_new <- NULL

3.3.2 Alarm Index Description

Here we merge the following factorial values of al_index_desc: Second Alarm, Third Alarm, 7-5 (All Hands Alarm), 10-76 & 10-77 Signal (Notification Hi-Rise Fire) and 10-75 Signal (Request for all hands alarm) into Others. But before doing so let’s see the actual distribution of values.

table(fire_data$al_index_desc)

## 
##       10-75 Signal (Request for all hands alarm) 
##                                               13 
## 10-76 & 10-77 Signal (Notification Hi-Rise Fire) 
##                                                3 
##                            7-5 (All Hands Alarm) 
##                                              100 
##                                   DEFAULT RECORD 
##                                            17313 
##                                    Initial Alarm 
##                                            32562 
##                                     Second Alarm 
##                                                8 
##                                      Third Alarm 
##                                                1

As we can see the two major category are DEFAULT RECORD and Initial Alarm, whereas the rest of categories occur very few time respect the main two, thus we decide again to merge them.

fire_data$alarm_level_idx_new <- fire_data$al_index_desc
levels(fire_data$alarm_level_idx_new) <- list(
  "DEFAULT RECORD" = "DEFAULT RECORD",
  "Initial Alarm" = "Initial Alarm", 
  "Others" = c("Second Alarm", "Third Alarm", "7-5 (All Hands Alarm)", 
               "10-76 & 10-77 Signal (Notification Hi-Rise Fire)",
               "10-75 Signal (Request for all hands alarm)")
)

ctable(fire_data$al_index_desc, fire_data$alarm_level_idx_new, prop = 'n', totals = FALSE, headings = FALSE)

## 
## -------------------------------------------------- --------------------- ---------------- --------------- --------
##                                                      alarm_level_idx_new   DEFAULT RECORD   Initial Alarm   Others
##                                      al_index_desc                                                                
##         10-75 Signal (Request for all hands alarm)                                      0               0       13
##   10-76 & 10-77 Signal (Notification Hi-Rise Fire)                                      0               0        3
##                              7-5 (All Hands Alarm)                                      0               0      100
##                                     DEFAULT RECORD                                  17313               0        0
##                                      Initial Alarm                                      0           32562        0
##                                       Second Alarm                                      0               0        8
##                                        Third Alarm                                      0               0        1
## -------------------------------------------------- --------------------- ---------------- --------------- --------

fire_data$al_index_desc <- fire_data$alarm_level_idx_new
fire_data$alarm_level_idx_new <- NULL

3.3.3 Alarm Source Description

Here we merge the following factorial values of al_source_desc: 911, 911TEXT, VERBAL, BARS, ERS, ERS-NC and SOL into Others. But before doing so let’s see the actual distribution of values.

table(fire_data$al_source_desc)

## 
##     911 911TEXT    BARS CLASS-3     EMS EMS-911     ERS  ERS-NC   PHONE     SOL 
##     302      14       1    5025   17178   10520     777       1   15146       5 
##  VERBAL 
##    1031

We decide to maintain as original the highest four and merge the rest into a new category.

fire_data$alarm_source_desc_new <- fire_data$al_source_desc
levels(fire_data$alarm_source_desc_new) <- list(
  "PHONE" = "PHONE",
  "EMS" = "EMS",
  "EMS-911" = "EMS-911",
  "CLASS-3" = "CLASS-3",
  "Others" = c("911", "911TEXT", "VERBAL", "BARS", "ERS", "ERS-NC", "SOL")
)

ctable(fire_data$al_source_desc, fire_data$alarm_source_desc_new, prop = 'n', totals = FALSE, headings = FALSE)

## 
## ---------------- ----------------------- ------- ------- --------- --------- --------
##                    alarm_source_desc_new   PHONE     EMS   EMS-911   CLASS-3   Others
##   al_source_desc                                                                     
##              911                               0       0         0         0      302
##          911TEXT                               0       0         0         0       14
##             BARS                               0       0         0         0        1
##          CLASS-3                               0       0         0      5025        0
##              EMS                               0   17178         0         0        0
##          EMS-911                               0       0     10520         0        0
##              ERS                               0       0         0         0      777
##           ERS-NC                               0       0         0         0        1
##            PHONE                           15146       0         0         0        0
##              SOL                               0       0         0         0        5
##           VERBAL                               0       0         0         0     1031
## ---------------- ----------------------- ------- ------- --------- --------- --------

fire_data$al_source_desc <- fire_data$alarm_source_desc_new
fire_data$alarm_source_desc_new <- NULL

View again the dataset summary to see the applied changes.

print(dfSummary(fire_data, 
                plain.ascii  = FALSE, 
                style        = "multiline", 
                headings     = FALSE,
                graph.magnif = 0.8, 
                valid.col    = FALSE),
                method = 'render')

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	id [character]	1. 230905-B0042-001-1051 2. 230905-B0053-001-0760 3. 230905-B0053-002-0910 4. 230905-B0081-001-1137 5. 230905-B0106-002-0632 6. 230905-B0132-001-0713 7. 230905-B0147-001-0967 8. 230905-B0160-001-1125 9. 230905-B0163-001-1026 10. 230905-B0165-001-0778 [ 49990 others ]	1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 1 ( 0.0% ) 49990 ( 100.0% )		0 (0.0%)
2	al_borough [factor]	1. Bronx 2. Brooklyn 3. Manhattan 4. Queens 5. Staten Island	10973 ( 21.9% ) 13980 ( 28.0% ) 12890 ( 25.8% ) 9879 ( 19.8% ) 2278 ( 4.6% )		0 (0.0%)
3	al_number [integer]	Mean (sd) : 2930.3 (2446.5) min ≤ med ≤ max: 10 ≤ 2275 ≤ 9933 IQR (CV) : 2772 (0.8)	7411 distinct values		0 (0.0%)
4	al_location [character]	1. 8 AVE & W 155 ST 2. 10 RICHMAN PLZ/SEDGWICK A 3. AMSTERDAM AVE & LA SALLE 4. 3 AVE & E 143 ST 5. WASHINGTON AVE & E 170 ST 6. FDR DR & E 6 ST 7. CONCOURSE VILLAGE E & E 1 8. PARK AVE & E 158 ST 9. UNION TPK & WINCHESTER BL 10. 8 AVE & W 33 ST [ 12203 others ]	85 ( 0.2% ) 75 ( 0.1% ) 50 ( 0.1% ) 48 ( 0.1% ) 48 ( 0.1% ) 45 ( 0.1% ) 44 ( 0.1% ) 40 ( 0.1% ) 40 ( 0.1% ) 39 ( 0.1% ) 49486 ( 99.0% )		0 (0.0%)
5	inc_borough [factor]	1. Bronx 2. Brooklyn 3. Manhattan 4. Queens 5. Staten Island	10973 ( 21.9% ) 13980 ( 28.0% ) 12890 ( 25.8% ) 9879 ( 19.8% ) 2278 ( 4.6% )		0 (0.0%)
6	zipcode [integer]	Mean (sd) : 10737.9 (551.8) min ≤ med ≤ max: 10000 ≤ 10472 ≤ 11697 IQR (CV) : 1098 (0.1)	217 distinct values		3181 (6.4%)
7	pol_prec [integer]	Mean (sd) : 62.3 (34.8) min ≤ med ≤ max: 1 ≤ 61 ≤ 123 IQR (CV) : 56 (0.6)	77 distinct values		3180 (6.4%)
8	city_con_dist [integer]	Mean (sd) : 23.1 (15.1) min ≤ med ≤ max: 1 ≤ 21 ≤ 51 IQR (CV) : 27 (0.7)	51 distinct values		3180 (6.4%)
9	commu_dist [integer]	Mean (sd) : 262.9 (119.4) min ≤ med ≤ max: 101 ≤ 302 ≤ 595 IQR (CV) : 206 (0.5)	70 distinct values		3180 (6.4%)
10	commu_sc_dist [integer]	Mean (sd) : 14.8 (9.7) min ≤ med ≤ max: 1 ≤ 13 ≤ 32 IQR (CV) : 18 (0.7)	32 distinct values		3182 (6.4%)
11	cong_dist [factor]	1. 3 2. 5 3. 6 4. 7 5. 8 6. 9 7. 10 8. 11 9. 12 10. 13 [ 3 others ]	634 ( 1.4% ) 3613 ( 7.7% ) 2309 ( 4.9% ) 4022 ( 8.6% ) 4539 ( 9.7% ) 3333 ( 7.1% ) 4768 ( 10.2% ) 2878 ( 6.1% ) 5089 ( 10.9% ) 5590 ( 11.9% ) 10045 ( 21.5% )		3180 (6.4%)
12	al_source_desc [factor]	1. PHONE 2. EMS 3. EMS-911 4. CLASS-3 5. Others	15146 ( 30.3% ) 17178 ( 34.4% ) 10520 ( 21.0% ) 5025 ( 10.1% ) 2131 ( 4.3% )		0 (0.0%)
13	al_index_desc [factor]	1. DEFAULT RECORD 2. Initial Alarm 3. Others	17313 ( 34.6% ) 32562 ( 65.1% ) 125 ( 0.2% )		0 (0.0%)
14	highest_al_level [factor]	1. First Alarm 2. NonFirst Alarm	49891 ( 99.8% ) 109 ( 0.2% )		0 (0.0%)
15	inc_class [factor]	1. Abandoned Derelict Vehicl 2. Alarm System - Defective 3. Alarm System - Testing 4. Alarm System - Unnecessar 5. Assist Civilian - Non-Med 6. Automobile Fire 7. Brush Fire 8. Carbon Monoxide - Code 1 9. Carbon Monoxide - Code 2 10. Carbon Monoxide - Code 3 [ 57 others ]	7 ( 0.0% ) 387 ( 0.8% ) 728 ( 1.5% ) 2845 ( 5.7% ) 4140 ( 8.3% ) 106 ( 0.2% ) 27 ( 0.1% ) 813 ( 1.6% ) 133 ( 0.3% ) 92 ( 0.2% ) 40722 ( 81.4% )		0 (0.0%)
16	inc_class_group [factor]	1. M_E 2. NonM_E 3. M_MFAs 4. NonMedical MFAs 5. NonS_F 6. Structural Fires	26824 ( 53.6% ) 19072 ( 38.1% ) 208 ( 0.4% ) 1680 ( 3.4% ) 703 ( 1.4% ) 1513 ( 3.0% )		0 (0.0%)
17	disp_resp_min_qy [numeric]	Mean (sd) : 0.7 (2.2) min ≤ med ≤ max: 0 ≤ 0.3 ≤ 150.4 IQR (CV) : 0.6 (3.3)	850 distinct values		0 (0.0%)
18	first_ass_datetime [POSIXct, POSIXt]	min : 2023-09-05 14:19:12 med : 2023-09-18 08:10:18.5 max : 2023-10-01 00:05:02 range : 25d 9H 45M 50S	49509 distinct values		0 (0.0%)
19	first_act_datetime [POSIXct, POSIXt]	min : 2023-09-05 14:19:26 med : 2023-09-18 08:09:01 max : 2023-10-01 00:05:16 range : 25d 9H 45M 50S	49205 distinct values		139 (0.3%)
20	first_onscene_datetime [POSIXct, POSIXt]	min : 2023-09-05 14:23:21 med : 2023-09-18 11:08:09.5 max : 2023-10-01 00:09:41 range : 25d 9H 46M 20S	35552 distinct values		14112 (28.2%)
21	inc_close_datetime [POSIXct, POSIXt]	min : 2023-09-05 14:25:05 med : 2023-09-18 08:35:24 max : 2023-10-01 00:58:42 range : 25d 10H 33M 37S	49409 distinct values		0 (0.0%)
22	disp_resp_min_indc [factor]	1. N 2. Y	50000 ( 100.0% ) 0 ( 0.0% )		0 (0.0%)
23	inc_resp_min_indc [factor]	1. N 2. Y	17036 ( 34.1% ) 32964 ( 65.9% )		0 (0.0%)
24	inc_resp_min_qy [numeric]	Mean (sd) : 6.4 (3.9) min ≤ med ≤ max: 0.3 ≤ 5.6 ≤ 118.8 IQR (CV) : 2.7 (0.6)	1497 distinct values		14112 (28.2%)
25	inc_travel_min_qy [numeric]	Mean (sd) : 5.7 (3.5) min ≤ med ≤ max: 0 ≤ 5 ≤ 118.7 IQR (CV) : 2.6 (0.6)	1369 distinct values		14112 (28.2%)
26	engines_assigned [integer]	Mean (sd) : 1.1 (0.8) min ≤ med ≤ max: 0 ≤ 1 ≤ 19 IQR (CV) : 0 (0.7)	15 distinct values		62 (0.1%)
27	ladders_assigned [integer]	Mean (sd) : 0.6 (0.8) min ≤ med ≤ max: 0 ≤ 0 ≤ 15 IQR (CV) : 1 (1.4)	12 distinct values		62 (0.1%)
28	others_units_assigned [integer]	Mean (sd) : 0.3 (0.8) min ≤ med ≤ max: 0 ≤ 0 ≤ 32 IQR (CV) : 0 (2.8)	23 distinct values		62 (0.1%)
29	emergency_min_qy [numeric]	Mean (sd) : 17.8 (27.7) min ≤ med ≤ max: 0 ≤ 12 ≤ 2615 IQR (CV) : 13.6 (1.6)	4429 distinct values		14112 (28.2%)
30	day_type [factor]	1. Weekday 2. Weekend	36482 ( 73.0% ) 13518 ( 27.0% )		0 (0.0%)
31	working_hour [factor]	1. FALSE 2. TRUE	21859 ( 43.7% ) 28141 ( 56.3% )		0 (0.0%)
32	time_of_day [factor]	1. Night 2. Morning 3. Afternoon 4. Evening	8521 ( 17.0% ) 13270 ( 26.5% ) 16499 ( 33.0% ) 11710 ( 23.4% )		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.2)
2024-01-18

3.4 Dealing with Invalid Values

The next step is to deal invalid values and delete some un-useful predictors.

3.4.1 Duplicate Columns

First of all we saw the possibility that al_borough and inc_borough represent the same column, let’s check it.

identical(fire_data$al_borough, fire_data$inc_borough)

## [1] TRUE

The column `al_borough and inc_borough have the same sequence of values, so we can delete one of the two.

fire_data <- fire_data %>% select(-c(al_borough))

3.4.2 Constant value for all observations

Then we say that all observation in the dataset have the indicator predictor disp_resp_min_indc equal to N, let’s check again and in affermative case then we can delete both columns.

summary(fire_data$disp_resp_min_indc)

##     N     Y 
## 50000     0

All our observations have non valid disp_resp_min_indc so we could delete both the column indicator and the respective column quantity disp_resp_min_qy. However we note that also in the original full dataset all the observation have the disp_resp_min_indc set to N, which is quite strange, and it seems like there is problem relative to the data acquisition, thus for the moment we decide to keep this time difference.

3.4.3 Validity Column Check

Now we do a quick check also on the other indicator predictor inc_resp_min_indc

summary(fire_data$inc_resp_min_indc)

##     N     Y 
## 17036 32964

But here we have some observations with valid inc_resp_min_indc, and we will consider only the valid one deleting the one that has a non valid attribute.

However before doing that let’s see the distribution of inc_resp_min_qy around the borough.

ggplot(data=fire_data %>% group_by(inc_borough, inc_resp_min_indc) %>% summarise(incident_number = n()), 
       aes(x=inc_borough, y=incident_number, fill=inc_resp_min_indc)) +
  geom_bar(stat="identity", position=position_dodge()) +
  geom_text(aes(label=incident_number), vjust=1.6, color="white",
            position = position_dodge(0.9), size=3.5) +
  scale_fill_brewer(palette="Paired") +
  labs(title = "Incident Count - Borouh - Valid Response Time in Minutes", x = "Borough", y = "Incident Number", fill = "Valid Response\n Time in Minutes")

## `summarise()` has grouped output by 'inc_borough'. You can override using the
## `.groups` argument.

We can see that the number of fire incident is higher for the valid response time in minutes with the top level from the Brooklyn borough. But it is much interesting observe the rateo between the valid and the non valid.

Here we see the rateo of valid inc_resp_min_indc in each borough:

rateo_inc_resp_min_indc <- fire_data %>% 
  group_by(inc_borough, inc_resp_min_indc) %>% 
  summarise(incident_number = n()) %>% 
  mutate(ratio=incident_number/sum(incident_number))

## `summarise()` has grouped output by 'inc_borough'. You can override using the
## `.groups` argument.

ggplot(rateo_inc_resp_min_indc, aes(fill=inc_resp_min_indc, y=ratio, x=inc_borough)) + 
  geom_bar(position="fill", stat="identity") + 
  geom_text(aes(label=scales::percent(ratio)), position=position_fill(vjust=0.5)) +
  labs(title="Borough - Rateo Incident between Valid and Invalid",
       x="Borough",
       y="Rateo Incident between Valid and Invalid",
       fill="Valid Response\nTime in Minutes")

And we can see that Staten Island has the higher number of incidents with valid inc_resp_min_indc , whereas Manhattan has the lower number, but remember that the former has the lowest number of fire incident and the latter has the higher number of incident.

Now we do an additional analysis to see if there is some relation between the inc_resp_min_indc and a new predictor the total_assigned_unit which is the sum of all the assigned units.

fire_data$total_assigned_unit <- fire_data$engines_assigned + fire_data$ladders_assigned + fire_data$others_units_assigned

ggplot(fire_data %>% drop_na(), aes(total_assigned_unit, inc_resp_min_qy)) + 
  geom_point(aes(colour = inc_resp_min_indc), na.rm = TRUE)+
  #scale_color_gradient(na.value = NA) + 
  labs(title = "Total Assigned Units - Response Time In Minutes", x = "Total Assigned Units",
        y = "Response Time In Minutes", colour = "Valid Response\n Time in Minutes")

We note that the majority of fire incident that had been assigned a single units has a high invalid response time. Whereas for an higher number of total units the response time decrease and becomes valid.

So we decide to have a look on the distribution of total assigned units respect to the response time for the observation that have invalid response time, all grouped by incident class.

ggplot(fire_data %>% filter(inc_resp_min_indc == "N") %>% drop_na()
            , aes(total_assigned_unit, inc_resp_min_qy)) + 
  geom_point(aes(colour = inc_class_group)) +
  labs(title = "Total Assigned Units - Response Time In Minutes - Incidnet Class Group", x = "Total Assigned Units", y = "Response Time In Minutes", colour = "Incident Class Groups")

And we see a majority of observation being of the incident class Medical Emergencies having assigned a single units. A closer look to the distribution of invalid response time for each incident class group summarised by the count of incident conferm what we already see in the previous points chart.

irt_i_class <- arrange(fire_data %>% filter(inc_resp_min_indc == "N") %>%
  group_by(inc_class_group) %>% summarise(incident_number = n()), desc(incident_number))
irt_i_class

## # A tibble: 6 × 2
##   inc_class_group  incident_number
##   <fct>                      <int>
## 1 M_E                        15116
## 2 NonM_E                      1589
## 3 NonMedical MFAs              162
## 4 Structural Fires              66
## 5 NonS_F                        59
## 6 M_MFAs                        44

Now we add a indicator column that tell us if the total assigned units is one or not. This would be useful in order to see possible relations.

# add an additional predictor
fire_data$tua_is_one <- as.factor(ifelse(fire_data$total_assigned_unit == 1, TRUE, FALSE))

See the relative incident count for each incident class.

irt_i_class_su <- arrange(fire_data %>% filter(inc_resp_min_indc == "N",  tua_is_one == TRUE) %>%
         group_by(inc_class_group) %>% summarise(incident_number = n()), desc(incident_number))
irt_i_class_su

## # A tibble: 5 × 2
##   inc_class_group incident_number
##   <fct>                     <int>
## 1 M_E                       14950
## 2 NonM_E                      836
## 3 NonMedical MFAs             128
## 4 M_MFAs                       28
## 5 NonS_F                       19

And we found that majority of all observation if not almost the entire dataset records are from the Medical Emergencies with only 166 incidents that have been assigned more than a single unit. Whereas almost all the other incidents are from the NonMedical Emergencies where in this case the incidents that have been assigned more than a single unit are more, so we have to take into account this higher amount of observations. Indeed we have 753 NonMedical Emergencies incidents that have invalid response time and have been assigned more than a single units.

At this point we decide to view the presence or not of pattern of incident with invalid response time that belong to the Medical Emergencies class with a single units assigned, first by grouping by borough.

tuaisone <- fire_data %>% 
        filter(inc_resp_min_indc == "N", inc_class_group == "Medical Emergencies") %>%
        group_by(inc_borough, tua_is_one) %>%
        summarise(incident_number = n())

## `summarise()` has grouped output by 'inc_borough'. You can override using the
## `.groups` argument.

Plot the bar chart.

ggplot(data=tuaisone, 
       aes(x=inc_borough, y=incident_number, fill=tua_is_one)) +
  geom_bar(stat="identity", position=position_dodge()) +
  geom_text(aes(label=incident_number), vjust=1.5, color="black",
            position = position_dodge(0.9), size=3.5) +
  scale_fill_brewer(palette="Set1") +
  labs(title = "Total Assigned Units One or Not for Invaid Resp Min Time",
       x = "Borough", y = "Incident Count", fill = "Total Assigned\nUnits are One")

And we can see that Bronx, Brooklyn and Manhattan have more or less the same amount of incident with invalid response time in minutes with a single assigned units.

Now we continue this analysis fo the invalid response time by changing the focus on the type of incident class (the sub-category which is more precise) with a single total assigned units.

ggplot(data=fire_data %>% 
        filter(inc_resp_min_indc == "N", inc_class_group == "Medical Emergencies", tua_is_one == TRUE) %>%
        group_by(inc_class, inc_borough) %>%
        summarise(incident_number = n()), 
       aes(x=inc_borough, y=incident_number, fill=inc_class)) + 
      geom_bar(stat="identity", position=position_dodge()) +
        geom_text(aes(label=incident_number), vjust=-0.5, color="black",
                  position = position_dodge(0.9), size=3) +
        scale_fill_brewer(palette="Set1") +
        labs(title = "Borough - Incident Counts - Incident Class -- for Total Assigned Units equal to 1 for Invalid Responde Time",
             x = "Borough", y = "Incident Counts", fill = "Incident Class Group")

## `summarise()` has grouped output by 'inc_class'. You can override using the
## `.groups` argument.

And we found that the majority of the incident that respect these circumstances are mostly identified as Medical - EMS Link 10-91 and Medical - PD Link 10-91.

Thanks to the 10code site we found a description of the two emergency codes:

1. 10-91 Medical Emergency EMS - Fire Unit Not Required - To be transmitted through borough dispatcher by the responding unit when the fire Unit is canceled enroute due to EMS on scene, or EMS downgrades the job to a segment that does not require a Fire Unit response. Note: This signal shall be used only for medical emergency incidents. EMS we are sure that stands for Emergency Medical Services.

2. 10-91 Medical Emergency PD - Fire Unit Not Required - To be transmitted through borough dispatcher by the responding unit when the fire Unit is canceled enroute due to PD on scene, or PD downgrades the job to a segment that does not require a Fire Unit response. Note: This signal shall be used only for medical emergency incidents. PD we think that stands for Police Department.

Thus we can trust this information since they make sense and consider only the observations that have inc_resp_min_indc == "Y".

Now we can look for the NonMedical Emergencies since are the second category for number of observation that by first see the distribution of its incident class.

Here we print the top 5 classes which respect the following conditions: inc_resp_min_indc == "N", inc_class_group == "NonMedical Emergencies"

print(head(arrange(fire_data %>% 
        filter(inc_resp_min_indc == "N", inc_class_group == "NonMedical Emergencies") %>%
        group_by(inc_class) %>%
        summarise(incident_number = n()), desc(incident_number))))

## # A tibble: 0 × 2
## # ℹ 2 variables: inc_class <fct>, incident_number <int>

And we found that the majority of non valid inc_resp_min_indc that are Non-Medical Emergency are from the incident class group equal to Assist Civilian - Non-Medical.

ggplot(data=fire_data %>% 
          filter(inc_resp_min_indc == "N", inc_class == "Assist Civilian - Non-Medical") %>%
          group_by(inc_borough) %>%
          summarise(incident_number = n()), 
        aes(x=inc_borough, y=incident_number)) + 
      geom_bar(stat="identity", position=position_dodge()) +
      geom_text(aes(label=incident_number), vjust=1.6, color="white", position = position_dodge(0.9), size=3.5) +
      labs(title = "Assist Civilian - Non-Medical / Invalid Response / More than 1 Units", x = "Borough", y = "Incident Count")

We can see that except Staten Island the amount of incident is pretty constant.

So for a stake of sempicity we decided to trust the indicator of incident response time and thus considering only the observations that have inc_resp_min_indc == "Y".

fire_data <- fire_data %>% filter(inc_resp_min_indc == "Y")
dim(fire_data)

## [1] 32964    33

3.4.4 Does the Incident Classes are unique for each Incident Class Group?

Now we want to know how many inc_class are summarized in each inc_class_group and if there are any intersection between any incident classes. We perform this analysis to be sure that each inc_class_group is referred to a single inc_class.

unique_category <- fire_data%>% 
  group_by(inc_class) %>%
  summarise(unique_maincategory = n_distinct((inc_class_group)))

conflict <- unique_category %>% filter(unique_maincategory > 1)

if (dim(conflict)[1] == 0){
  print("There are NO conflict between main and sub-category")
} else {
  print("There are conflict between main and sub-category")
}

## [1] "There are NO conflict between main and sub-category"

So we are sure that each sub category has a unique main-category incident.

At this point to be more clear we display each main class with each respective sub-class.

for (variable in levels(fire_data$inc_class_group)) {
  non_zero_table <- table(subset(fire_data, inc_class_group == variable)$inc_class)
  cat(variable, "\n")
  print(non_zero_table[non_zero_table != 0])
  cat("\n")
}

## M_E 
## 
##              Medical - Assist Civilian      Medical - Breathing / Ill or Sick 
##                                     27                                   4779 
##               Medical - EMS Link 10-91 Medical - No PT Contact EMS is Onscene 
##                                   1096                                   4285 
##                Medical - PD Link 10-91     Medical - Serious Life Threatening 
##                                    868                                    366 
##              Medical - Victim Deceased 
##                                    287 
## 
## NonM_E 
## 
##                          Alarm System - Defective 
##                                               377 
##                            Alarm System - Testing 
##                                               706 
##                        Alarm System - Unnecessary 
##                                              2735 
##                     Assist Civilian - Non-Medical 
##                                              3312 
##          Carbon Monoxide - Code 1 - Investigation 
##                                               788 
##     Carbon Monoxide - Code 2 - Incident (1-9 ppm) 
##                                               129 
## Carbon Monoxide - Code 3 - Emergency (over 9 ppm) 
##                                                88 
## Carbon Monoxide - Code 4 - No Detector Activation 
##                                                 8 
##                              Defective Oil Burner 
##                                                34 
##                                       Downed Tree 
##                                               280 
##                     Elevator Emergency - Occupied 
##                                              1850 
##                   Elevator Emergency - Unoccupied 
##                                               708 
##             Non-Medical 10-91 (Unnecessary Alarm) 
##                                               102 
##                                Odor - Other Smoke 
##                                               166 
##                           Odor - Other Than Smoke 
##                                              1317 
##                        Remove Civilian - Non-Fire 
##                                                27 
##                      Sprinkler System - Activated 
##                                                 6 
##                    Sprinkler System - Malfunction 
##                                                41 
##              Sprinkler System - Working on System 
##                                                28 
##                          Transit System Emergency 
##                                                18 
##                               Undefined Emergency 
##                                                71 
##                      Utility Emergency - Electric 
##                                               595 
##                           Utility Emergency - Gas 
##                                              1335 
##                         Utility Emergency - Steam 
##                                               137 
##                     Utility Emergency - Undefined 
##                                                 4 
##                         Utility Emergency - Water 
##                                              1157 
##                          Vehicle Accident - Other 
##                                              1443 
##               Vehicle Accident - With Extrication 
##                                                21 
## 
## M_MFAs 
## 
## Medical MFA - EMS Link  Medical MFA - PD Link 
##                     87                     77 
## 
## NonMedical MFAs 
## 
##                Non-Medical MFA - ERS     Non-Medical MFA - ERS No Contact 
##                                  586                                    1 
##              Non-Medical MFA - Phone Non-Medical MFA - Private Fire Alarm 
##                                  701                                  223 
##             Non-Medical MFA - Verbal 
##                                    7 
## 
## NonS_F 
## 
##   Abandoned Derelict Vehicle Fire                   Automobile Fire 
##                                 6                               101 
##                        Brush Fire Demolition Debris or Rubbish Fire 
##                                24                               272 
##        Manhole Fire - Blown Cover              Manhole Fire - Other 
##                                 9                                55 
##      Manhole Fire - Seeping Smoke         Other Transportation Fire 
##                               104                                14 
##    Transit System - NonStructural 
##                                59 
## 
## Structural Fires 
## 
##                                    Church Fire 
##                                             10 
##                                   Factory Fire 
##                                              1 
##                                  Hospital Fire 
##                                             18 
##         Multiple Dwelling 'A' - Compactor fire 
##                                              4 
## Multiple Dwelling 'A' - Food on the stove fire 
##                                            519 
##             Multiple Dwelling 'A' - Other fire 
##                                            168 
##                     Multiple Dwelling 'B' Fire 
##                                             85 
##                 Other Commercial Building Fire 
##                                            184 
##                     Other Public Building Fire 
##                                              4 
##                          Private Dwelling Fire 
##                                            412 
##                                    School Fire 
##                                             31 
##                                     Store Fire 
##                                              9 
##                    Transit System - Structural 
##                                              1 
##                Under Contruction / Vacant Fire 
##                                              1

4 Dealing With Missing Data

At this point is essential to deal with NA values, trying to find the presence of possible relation with predictors. First things first let’s recap the number of NA values for each columns that we have at the moment.

colSums(is.na(fire_data))

##                     id              al_number            al_location 
##                      0                      0                      0 
##            inc_borough                zipcode               pol_prec 
##                      0                   2197                   2197 
##          city_con_dist             commu_dist          commu_sc_dist 
##                   2197                   2197                   2198 
##              cong_dist         al_source_desc          al_index_desc 
##                   2197                      0                      0 
##       highest_al_level              inc_class        inc_class_group 
##                      0                      0                      0 
##       disp_resp_min_qy     first_ass_datetime     first_act_datetime 
##                      0                      0                     41 
## first_onscene_datetime     inc_close_datetime     disp_resp_min_indc 
##                      0                      0                      0 
##      inc_resp_min_indc        inc_resp_min_qy      inc_travel_min_qy 
##                      0                      0                      0 
##       engines_assigned       ladders_assigned  others_units_assigned 
##                      4                      4                      4 
##       emergency_min_qy               day_type           working_hour 
##                      0                      0                      0 
##            time_of_day    total_assigned_unit             tua_is_one 
##                      0                      4                      4

4.1 Geographical Columns

Here we will check if there is a pattern on the absence of values in the following predictors: zipcode, pol_prec, city_con_dist, commu_dist, commu_sc_dist and cong_dist.

na_locations <- fire_data %>%
  filter(is.na(zipcode) | is.na(pol_prec) | is.na(city_con_dist) | is.na(commu_dist) | is.na(commu_sc_dist) | is.na(cong_dist))

ggplot(data=na_locations %>% 
        group_by(inc_class_group, inc_borough) %>%
        summarise(incident_number = n()), 
       aes(x=inc_borough, y=incident_number, fill=inc_class_group)) + geom_bar(stat="identity", position=position_dodge()) +
        geom_text(aes(label=incident_number), vjust=-0.5, color="black",
                  position = position_dodge(0.9), size=3.5) +
        scale_fill_brewer(palette="Set1") +
        labs(title = "NA Locations Columns", x = "Borough", y = "Incident Count", fill = "Incident Class Group")

## `summarise()` has grouped output by 'inc_class_group'. You can override using
## the `.groups` argument.

By the Bar Chart we note that the majority of observations that have at least one of the location predictors to NA are of the incident class group NonMedical Emergency, Medical Emergencies and Non Medical MFAs.

Here we verify if the proportion of NA is equally distributed around each borough.

table(na_locations$inc_borough) / table(fire_data$inc_borough)

## 
##         Bronx      Brooklyn     Manhattan        Queens Staten Island 
##    0.07104538    0.04694547    0.07662157    0.07700328    0.07523697

And we found that Brooklyn has a lower percentage of NA location observation respect the others that are constant between them.

Thus we compute the same process but this time looking into the incident class group, like before we would like to have a constant distribution among all classes.

table(na_locations$inc_class_group) / table(fire_data$inc_class_group)

## 
##              M_E           NonM_E           M_MFAs  NonMedical MFAs 
##       0.03988726       0.05691243       0.10975610       0.40447958 
##           NonS_F Structural Fires 
##       0.14906832       0.00552868

However by looking at the proportion with the original dataset around the 40% of the whole observations of type NonMedical MFAs have at least one of the location columns to NA. Let’s investigate.

In the original dataset this is the distribution of incident class for the NonMedical MFA incident class group.

fd_nm_mfa_cl <- table(subset(fire_data, inc_class_group == "NonMedical MFAs")$inc_class)
fd_nm_mfa_bro <- table(subset(fire_data, inc_class_group == "NonMedical MFAs")$inc_borough)

fd_nm_mfa_cl <- fd_nm_mfa_cl[fd_nm_mfa_cl != 0]
fd_nm_mfa_cl

## 
##                Non-Medical MFA - ERS     Non-Medical MFA - ERS No Contact 
##                                  586                                    1 
##              Non-Medical MFA - Phone Non-Medical MFA - Private Fire Alarm 
##                                  701                                  223 
##             Non-Medical MFA - Verbal 
##                                    7

And this is the distribution of incident class for the NonMedical MFA incident class group, but this time w.r.t. the subset of incidents that have at least one locaton column to NA.

na_nm_mfa_cl <- table(subset(na_locations, inc_class_group == "NonMedical MFAs")$inc_class)
na_nm_mfa_bro <- table(subset(na_locations, inc_class_group == "NonMedical MFAs")$inc_borough)

na_nm_mfa_cl <- na_nm_mfa_cl[names(fd_nm_mfa_cl)]
na_nm_mfa_cl

## 
##                Non-Medical MFA - ERS     Non-Medical MFA - ERS No Contact 
##                                  573                                    1 
##              Non-Medical MFA - Phone Non-Medical MFA - Private Fire Alarm 
##                                   38                                    2 
##             Non-Medical MFA - Verbal 
##                                    0

And we can clearly see that the observations of category Non-Medical MFA - ERS are the main.

na_nm_mfa_cl / fd_nm_mfa_cl

## 
##                Non-Medical MFA - ERS     Non-Medical MFA - ERS No Contact 
##                           0.97781570                           1.00000000 
##              Non-Medical MFA - Phone Non-Medical MFA - Private Fire Alarm 
##                           0.05420827                           0.00896861 
##             Non-Medical MFA - Verbal 
##                           0.00000000

And more interestingly the 97% of all the Non-Medical MFA - ERS observations in the entire dataset have one of the location attribute equal to NA.

na_nm_mfa_bro / fd_nm_mfa_bro

## 
##         Bronx      Brooklyn     Manhattan        Queens Staten Island 
##     0.4676056     0.3075221     0.3894472     0.3733333     0.7954545

And from here we can see that about the 78% of the observations that are NonMedical - MFAs that have at least one district column attribute to NA are from the RICHMOND / STATEN ISLAND. Also BRONX has about half of the NonMedical - MFAs observations that have at least one district column to NA.

ggplot(data=na_locations %>%
         filter(inc_class == "Non-Medical MFA - ERS") %>%
         group_by(inc_borough) %>%
         summarise(incident_count = n()), 
      aes(x=inc_borough, y=incident_count)) + 
      geom_bar(stat="identity", position=position_dodge()) +
      geom_text(aes(label=incident_count), vjust=1.6, color="white", position = position_dodge(0.9), size=3.5) +
      labs(title = "Non-Medical MFA - ERS", x = "Borough", y = "Incident Count")

Finally we have that we have a lower number of Non-Medical MFA - ERS for Queens and Staten Istand.

So concluding NonMedical MFA stands for Non Medical - Medical First Aid, unfortunately we are not able to find the meaning of ERS, even if we think that could be possible be connected with something regarding the respiratory system like ERS: Emergency Respiratory System. And thus we think that these observations have NA location column by the same reason of the previously discussed 10-91 Medical Emergency. However these are all suppositions, maybe by contacting the NYC Firefighters Department by mail should make things much clear.

Again for stake of semplicity we have assumed that the observations that have at least one NA locations are randomly spreaded among the dataset.

4.2 Assigned Units Column

print(fire_data %>%
  filter(is.na(engines_assigned) | is.na(ladders_assigned) | is.na(others_units_assigned)))

##                      id al_number                    al_location inc_borough
## 1 230905-Q4545-001-0683      4545                 53 AVE & 69 ST      Queens
## 2 230914-Q1014-001-0693      1014 CENTRAL AVE & VIRGINIA ST/LCFD      Queens
## 3 230918-Q9643-002-0752      9643           JAMAICA AVE & 114 ST      Queens
## 4 230919-M0684-002-0995       684                1 AVE & E 30 ST   Manhattan
##   zipcode pol_prec city_con_dist commu_dist commu_sc_dist cong_dist
## 1   11378      104            30        405            24         6
## 2   11691      101            31        414            27         5
## 3   11418      102            29        409            27         5
## 4   10016       13             4        106             2        12
##   al_source_desc  al_index_desc highest_al_level
## 1            EMS  Initial Alarm      First Alarm
## 2        EMS-911 DEFAULT RECORD      First Alarm
## 3          PHONE  Initial Alarm      First Alarm
## 4         Others  Initial Alarm      First Alarm
##                                inc_class inc_class_group disp_resp_min_qy
## 1 Medical - No PT Contact EMS is Onscene             M_E        0.2666667
## 2                Medical - PD Link 10-91             M_E        0.1000000
## 3          Assist Civilian - Non-Medical          NonM_E        0.5833333
## 4               Vehicle Accident - Other          NonM_E        0.7000000
##    first_ass_datetime  first_act_datetime first_onscene_datetime
## 1 2023-09-05 16:32:44 2023-09-05 16:33:34    2023-09-05 16:37:22
## 2 2023-09-14 22:57:58 2023-09-14 22:58:16    2023-09-14 23:02:24
## 3 2023-09-18 23:44:39 2023-09-18 23:45:11    2023-09-18 23:50:08
## 4 2023-09-19 16:35:57 2023-09-19 16:36:07    2023-09-19 16:46:50
##    inc_close_datetime disp_resp_min_indc inc_resp_min_indc inc_resp_min_qy
## 1 2023-09-05 16:40:41                  N                 Y        4.900000
## 2 2023-09-14 23:05:08                  N                 Y        4.533333
## 3 2023-09-19 00:07:10                  N                 Y        6.066667
## 4 2023-09-19 16:56:33                  N                 Y       11.583333
##   inc_travel_min_qy engines_assigned ladders_assigned others_units_assigned
## 1          4.633333               NA               NA                    NA
## 2          4.433333               NA               NA                    NA
## 3          5.483333               NA               NA                    NA
## 4         10.883333               NA               NA                    NA
##   emergency_min_qy day_type working_hour time_of_day total_assigned_unit
## 1         3.316667  Weekday         TRUE   Afternoon                  NA
## 2         2.733333  Weekday        FALSE     Evening                  NA
## 3        17.033333  Weekday        FALSE     Evening                  NA
## 4         9.716667  Weekday         TRUE   Afternoon                  NA
##   tua_is_one
## 1       <NA>
## 2       <NA>
## 3       <NA>
## 4       <NA>

We can easily remove this observations, since it not appear any pattern and are only 4.

4.3 First Act Datetime

Now we look into First Act Datetime and see if there is any pattern with the other columns.

na_first_act_datetime <- fire_data %>% filter(is.na(first_act_datetime))

print(arrange(na_first_act_datetime %>% group_by(inc_class, inc_borough) %>% summarise(incident_count = n()), desc(incident_count)))

## `summarise()` has grouped output by 'inc_class'. You can override using the
## `.groups` argument.

## # A tibble: 27 × 3
## # Groups:   inc_class [15]
##    inc_class                              inc_borough incident_count
##    <fct>                                  <fct>                <int>
##  1 Assist Civilian - Non-Medical          Brooklyn                 4
##  2 Medical - Breathing / Ill or Sick      Brooklyn                 4
##  3 Medical - Breathing / Ill or Sick      Manhattan                4
##  4 Medical - No PT Contact EMS is Onscene Queens                   3
##  5 Non-Medical MFA - ERS                  Brooklyn                 3
##  6 Alarm System - Unnecessary             Brooklyn                 2
##  7 Assist Civilian - Non-Medical          Bronx                    1
##  8 Assist Civilian - Non-Medical          Queens                   1
##  9 Demolition Debris or Rubbish Fire      Brooklyn                 1
## 10 Downed Tree                            Manhattan                1
## # ℹ 17 more rows

ggplot(data=na_first_act_datetime %>% 
        group_by(inc_class_group, inc_borough) %>%
        summarise(incident_count = n()), 
    aes(x=inc_borough, y=incident_count, fill=inc_class_group)) +
    geom_bar(stat="identity", position=position_dodge()) +
    geom_text(aes(label=incident_count), vjust=1.6, color="white", position = position_dodge(0.9), size=3.5) +
    labs(title = "NA First Act Date", x = "Borough", y = "Incident Count", fill = "Incident Class Group")

## `summarise()` has grouped output by 'inc_class_group'. You can override using
## the `.groups` argument.

Seems to be random and thus there is no pattern that motivate the presence of NA values in first_act_datetime.

4.4 Performing na.omit

At this point we can omit the NA values.

fire_data_new <- na.omit(fire_data)

And the un-usefull predictors including disp_resp_min_indc and inc_travel_min_qy by since these two information are contained in inc_resp_min_qy.

fire_data_new <- fire_data_new %>% select(-c(zipcode, pol_prec, city_con_dist, commu_dist, al_location,
                                             commu_sc_dist, first_ass_datetime, first_act_datetime,
                                             first_onscene_datetime, inc_close_datetime, inc_resp_min_indc,
                                             disp_resp_min_indc, inc_travel_min_qy, disp_resp_min_qy, 
                                             id, al_number, inc_class))

We decide to remove also the incident class since it contains too much category values which are summarised in the incident class group.

print(dfSummary(fire_data_new, 
                plain.ascii  = FALSE, 
                style        = "multiline", 
                headings     = FALSE,
                graph.magnif = 0.8, 
                valid.col    = FALSE),
                method = 'render')

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	inc_borough [factor]	1. Bronx 2. Brooklyn 3. Manhattan 4. Queens 5. Staten Island	6406 ( 20.8% ) 9280 ( 30.2% ) 7294 ( 23.7% ) 6188 ( 20.1% ) 1560 ( 5.1% )		0 (0.0%)
2	cong_dist [factor]	1. 3 2. 5 3. 6 4. 7 5. 8 6. 9 7. 10 8. 11 9. 12 10. 13 [ 3 others ]	458 ( 1.5% ) 2420 ( 7.9% ) 1532 ( 5.0% ) 2907 ( 9.5% ) 3135 ( 10.2% ) 2257 ( 7.3% ) 3212 ( 10.5% ) 2073 ( 6.7% ) 2909 ( 9.5% ) 3504 ( 11.4% ) 6321 ( 20.6% )		0 (0.0%)
3	al_source_desc [factor]	1. PHONE 2. EMS 3. EMS-911 4. CLASS-3 5. Others	13465 ( 43.8% ) 7369 ( 24.0% ) 4349 ( 14.2% ) 4817 ( 15.7% ) 728 ( 2.4% )		0 (0.0%)
4	al_index_desc [factor]	1. DEFAULT RECORD 2. Initial Alarm 3. Others	2979 ( 9.7% ) 27631 ( 89.9% ) 118 ( 0.4% )		0 (0.0%)
5	highest_al_level [factor]	1. First Alarm 2. NonFirst Alarm	30626 ( 99.7% ) 102 ( 0.3% )		0 (0.0%)
6	inc_class_group [factor]	1. M_E 2. NonM_E 3. M_MFAs 4. NonMedical MFAs 5. NonS_F 6. Structural Fires	11222 ( 36.5% ) 16471 ( 53.6% ) 146 ( 0.5% ) 904 ( 2.9% ) 547 ( 1.8% ) 1438 ( 4.7% )		0 (0.0%)
7	inc_resp_min_qy [numeric]	Mean (sd) : 5.9 (2.7) min ≤ med ≤ max: 0.3 ≤ 5.5 ≤ 58.9 IQR (CV) : 2.5 (0.5)	1147 distinct values		0 (0.0%)
8	engines_assigned [integer]	Mean (sd) : 1.1 (0.9) min ≤ med ≤ max: 0 ≤ 1 ≤ 19 IQR (CV) : 0 (0.8)	15 distinct values		0 (0.0%)
9	ladders_assigned [integer]	Mean (sd) : 0.8 (0.8) min ≤ med ≤ max: 0 ≤ 1 ≤ 15 IQR (CV) : 1 (1)	12 distinct values		0 (0.0%)
10	others_units_assigned [integer]	Mean (sd) : 0.4 (0.9) min ≤ med ≤ max: 0 ≤ 0 ≤ 32 IQR (CV) : 1 (2.3)	22 distinct values		0 (0.0%)
11	emergency_min_qy [numeric]	Mean (sd) : 17.1 (19.3) min ≤ med ≤ max: 0 ≤ 11.9 ≤ 596.4 IQR (CV) : 12.4 (1.1)	4040 distinct values		0 (0.0%)
12	day_type [factor]	1. Weekday 2. Weekend	22379 ( 72.8% ) 8349 ( 27.2% )		0 (0.0%)
13	working_hour [factor]	1. FALSE 2. TRUE	12677 ( 41.3% ) 18051 ( 58.7% )		0 (0.0%)
14	time_of_day [factor]	1. Night 2. Morning 3. Afternoon 4. Evening	4487 ( 14.6% ) 8370 ( 27.2% ) 10627 ( 34.6% ) 7244 ( 23.6% )		0 (0.0%)
15	total_assigned_unit [integer]	Mean (sd) : 2.3 (2.2) min ≤ med ≤ max: 1 ≤ 1 ≤ 66 IQR (CV) : 2 (1)	34 distinct values		0 (0.0%)
16	tua_is_one [factor]	1. FALSE 2. TRUE	12940 ( 42.1% ) 17788 ( 57.9% )		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.2)
2024-01-18

5 Data Visaulisation

In this section we will have a look on additional data visualisation in order to better understand how the predictors behaves. We divide this section in multiple subsection each focus on a relative subset of columns.

See the final columns that we have:

names(fire_data_new)

##  [1] "inc_borough"           "cong_dist"             "al_source_desc"       
##  [4] "al_index_desc"         "highest_al_level"      "inc_class_group"      
##  [7] "inc_resp_min_qy"       "engines_assigned"      "ladders_assigned"     
## [10] "others_units_assigned" "emergency_min_qy"      "day_type"             
## [13] "working_hour"          "time_of_day"           "total_assigned_unit"  
## [16] "tua_is_one"

We divide the data visualisation in the following subsections:

1. Location Information
2. Alarm Information
3. Assigned Units Information
4. Time Information
5. Day Information

In each section analysis we will use both response: inc_resp_min_qy adn emergency_time_qy.

5.1 Location Information

Here we study the inc_borough and cong_dist columns for both responses.

5.1.1 Incident Borough

ggplot(fire_data_new,
       aes(x = inc_borough, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Borough - Incident Minutes Response Time - Incident Class",
       x = "Borough", y = "Incident Minutes Response Time", color = "Incident Class")

Here we can see that the response time is pretty the same around all borough for each incident class, indeed there is a slightly fast response respect to the other class for the Structural Fires as expected. The the highest number of outliers are from the Medical and NonMedical Emergencies.

ggplot(fire_data_new,
       aes(x = inc_borough, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Borough - Emergency Minutes Time - Incident Class",
       x = "Borough", y = "Emergency Minutes Time", color = "Incident Class")

Like before the Structural Fires require lots of time but only in some drastic cases identified by the outliers since on average they require even less time respect other categories.

5.1.2 Congressional District

bp1 <- ggplot(fire_data_new,
       aes(x = cong_dist, y = inc_resp_min_qy)) +
  geom_boxplot() +
  labs(title = "Cong District - EmergencyMin Time",
       x = "Cong District", y = "Emergency Min Time")

bp2 <- ggplot(fire_data_new,
       aes(x = cong_dist, y = emergency_min_qy)) +
  geom_boxplot() +
  labs(title = "Cong District - Emergency Min Time",
       x = "Cong District", y = "Emergency Minutes Time")

grid.arrange(bp1, bp2, ncol = 2)

Here we can’t see any particular relevent information since on average we have the same distribution of time for all congressional district.

5.2 Alarm Information

Here we study the al_source_desc, al_index_desc and highest_al_level columns for both responses.

5.2.1 Alarm Source Description

ggplot(fire_data_new,
       aes(x = al_source_desc, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Alarm Source Description - Incident Minutes Response Time - Incident Class",
       x = "Alarm Source Description", y = "Incident Minutes Response Time", color = "Incident Class")

With this boxplot we can understand that there are many outliers for emergencies called via phone, in particular NonMedical Emergencies. Also in both EMS and EMS-911 there are many outliers for the Medical Emergencies. As expected then for emergencies called via EMS-911 we do not find both Fires emergencies and NonMedical MFAs.

ggplot(fire_data_new,
       aes(x = al_source_desc, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Alarm Source Description - Emergency Minutes Time - Incident Class",
       x = "Alarm Source Description", y = "Emergency Minutes Time", color = "Incident Class")

Now the first thing that we see is the high amount of outliers for the emergencies called via phone that are structural fires, indeed on average the emergency time is higher for that alarm source category. An other interesting fact is for CLASS-3 the NonMedical MFAs have a big inter-quantile range, since we have few information for that particular class.

5.2.2 Alarm Index Description

ggplot(fire_data_new,
       aes(x = al_index_desc, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Alarm Index Description - Incident Minutes Response Time - Incident Class",
       x = "Alarm Index Description", y = "Incident Minutes Response Time", color = "Incident Class")

Again we can see many outliers for the two major class, and for the Initial Alarm we do not have any Medical and NonMedical MFAs emergencies. For the Others class like in the previous cans the inter-quantile range for the NonStructural Fires is big since in the merged category we do no have many observations.

ggplot(fire_data_new,
       aes(x = al_index_desc, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Alarm Index Description - Emergency Minutes Time - Incident Class",
       x = "Alarm Index Description", y = "Emergency Minutes Time", color = "Incident Class")

Interestingly here we can see that around all the emergency with index level DEFAULT RECORD have very thin inter-quantile range with also low outliers, respect to the Initial Alarm and Others category level.

5.2.3 Highest Alarm Level

ggplot(fire_data_new,
       aes(x = highest_al_level, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Highest Alarm Level - Incident Minutes Response Time - Incident Class",
       x = "Highest Alarm Level", y = "Incident Minutes Response Time", color = "Incident Class")

Pretty all emergency of First Alarm have the same amount of response time with particular outliers for Medical and NonMedical Emergencies and also for NonMedical MFAs, whereas for NonFirst Alarm since we have few observation there for the NonStructural Fire we have a big range of interquantile.

ggplot(fire_data_new,
       aes(x = highest_al_level, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Highest Alarm Level - Emergency Minutes Time - Incident Class",
       x = "Highest Alarm Level", y = "Emergency Minutes Time", color = "Incident Class")

Similarly to the previous subsection here all the emergency with highest alarm level to First Alarm have a low emergency time in particular Medical and NonMedical MFAs, again we have a significative number of outliers for this class. Regarding NonFirst Level we have few observations and that is why the emergency time is so different respect to the other class. Even if the longest time taken emergencies are a structural fire with highest alarm level NonFirst Alarm.

5.3 Assigned Units Information

Here we study the total_assigned_unit and tua_is_one columns for both responses.

5.3.1 Total Units Assigned

ggplot(fire_data_new,
       aes(x = total_assigned_unit, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_point(aes(color = inc_class_group)) +
  labs(title = "Total Units Assigned - Incident Minutes Response Time - Incident Class",
       x = "Total Units Assigned", y = "Incident Minutes Response Time", color = "Incident Class")

Whereas here as described in a previous similar chart, the total assigned units and the incident response time in minutes appear to be inversely proportional, with the contradistinction of structural fires that have many assigned units and low response time and Non Medical Emergencies that have low assigned units and high response time.

ggplot(fire_data_new,
       aes(x = total_assigned_unit, y = emergency_min_qy, color = inc_class_group)) +
  geom_point(aes(color = inc_class_group)) +
  labs(title = "Total Units Assigned - Emergency Minutes Time - Incident Class",
       x = "Total Units Assigned", y = "Emergency Minutes Time", color = "Incident Class")

Here we can see that the Structural Fire incident have lots of variance directly and seems be a directly proportional relationship between the total assigned units and the Emergency Time. For the NonMedical Emergencies they are clustered and then for the Medical Emergencies we can see that they have been mostly assigend a single units.

5.3.2 Total Assigned Units is One

ggplot(fire_data_new,
       aes(x = tua_is_one, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Total Assigned Unit is 1 - Incident Minutes Response Time - Incident Class",
       x = "Total Assigned Unit is 1", y = "Incident Minutes Response Time", color = "Incident Class")

Here we can see a quite difference respect to having a total assigned unit equal to one and not, indeed every incident class have a higher mean value for the incident which had been assigend a single units.

ggplot(fire_data_new,
       aes(x = tua_is_one, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Total Assigned Unit is 1 - Emergency Minutes Time - Incident Class",
       x = "Total Assigned Unit is 1", y = "Emergency Minutes Time", color = "Incident Class")

And in this case we can see that there is less variation on the emergency time response for the incident that had been assigned a single units, respect to the incident with more units assigned. And as expected the Structural Fires with more than one units are the type of incident with most outliers given their severity.

5.4 Time Information

Here we study the relation of inc_resp_min_qy against emergency_time_qy.

ggplot(fire_data_new,
       aes(x = inc_resp_min_qy, y = emergency_min_qy, color = inc_class_group)) +
  geom_point(aes(color = inc_class_group)) +
  labs(title = "Incident Minutes Response Time - Emergency Minutes Time - Incident Class",
       x = "Incident Minutes Response Time", y = "Emergency Minutes Time", color = "Incident Class")

Here we can see many different distribution with the relation that the less the response time is the higher is the time taken to close the emergency.

Since we can’t understand much by this chart we decide to plot the density of each incident class group for both response.

ggplot(fire_data_new, aes(x = inc_resp_min_qy, fill = inc_class_group)) +
  geom_density(alpha = 0.3) + xlim(0, 20) +
  labs(title = " Density Incident Minutes Response Time - Incident Class",
       x = "Incident Minutes Response Time", y = "Density", fill = "Incident Class")

## Warning: Removed 129 rows containing non-finite values (`stat_density()`).

In this density chart we can see that the distribution of Incident Response Time for each incident class seems to behave like a Log-Norm distribution. We limit the x axis in order to be able to understand the distribution otherwise it will result in a very thin curve so not much easy to understand.

ggplot(fire_data_new, aes(x = emergency_min_qy, fill = inc_class_group)) +
  geom_density(alpha = 0.5) + xlim(0, 75) +
  scale_fill_brewer(palette="Set1") +
  labs(title = "Density Emergency Response Time - Incident Class",
       x = "Emergency Response Time", y = "Density", fill = "Incident Class")

## Warning: Removed 482 rows containing non-finite values (`stat_density()`).

Similarly as before also here the distribution of Incident Time for each incident class seems to behave like a Log-Norm distribution. Again lke before we limit the x axis in order to be able to understand the distribution otherwise it will result in a very thin curve so not much easy to understand.

5.5 Day Information

Here we study the day_type, working_hour and time_of_day columns for both responses.

5.5.1 Day Type

ggplot(fire_data_new,
       aes(x = day_type, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Day Type - Incident Minutes Response Time - Incident Class",
       x = "Day Type", y = "Incident Minutes Response Time", color = "Incident Class")

Speaking about the day we can’t see any relevant pattern here except that it appears in the weekend we have a lower number of outliers for all the incident class.

ggplot(fire_data_new,
       aes(x = day_type, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Day Type - Emergency Minutes Time - Incident Class",
       x = "Day Type", y = "Emergency Minutes Time", color = "Incident Class")

Here there is any kind of patters that we can recondunct to the day type for the emergency time taken.

5.5.2 Working Hour

ggplot(fire_data_new,
       aes(x = working_hour, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Working Hour - Incident Minutes Response Time - Incident Class",
       x = "Working Hour", y = "Incident Minutes Response Time", color = "Incident Class")

Even for the working hourwe can’t see any pattern regarding the incident response time.

ggplot(fire_data_new,
       aes(x = working_hour, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Working Hour - Emergency Minutes Time - Incident Class",
       x = "Working Hour", y = "Incident Minutes Time", color = "Incident Class")

And even for the emergency time taken.

5.5.3 Day Time

ggplot(fire_data_new,
       aes(x = time_of_day, y = inc_resp_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Day Time - Incident Minutes Response Time - Incident Class",
       x = "Day Time", y = "Incident Minutes Response Time", color = "Incident Class")

Here we can’t see any relevant pattern except the fact that we have an higher number of outliers in the afternoon for the Medical, NonMedical Emergencies and Structural Fires.

ggplot(fire_data_new,
       aes(x = time_of_day, y = emergency_min_qy, color = inc_class_group)) +
  geom_boxplot() +
  labs(title = "Day Time - Emergency Minutes Time - Incident Class",
       x = "Day Time", y = "Incident Minutes Time", color = "Incident Class")

Here interestingly we can see that the longest time taken emergencies are Structural Fires and they happened in the Night and in the Morning. Except this we do not have any pattern.

5.5.4 Maps Visualization

In this section we plot additional data visualization focus on the geographical visualization of the New York borough and congressional distirct with relative predictors. In order to do so we load an additional datasets:

1. The fdny-firehouse-listing.csv is a dataset that includes the geographical informations of every firefighter stations in the NYC, including again latitude and longitude.
2. NYC_BoroughBoundaries.geojson is the spatial dataframe that includes the geometry of each boroughs
3. Congressiona_Districts.geojson is the spatial dataframe that includes the geometry of each congressional districts

firefighter_stations <- read.csv("datasets/fdny-firehouse-listing.csv")

head(firefighter_stations)

##                                              FacilityName       FacilityAddress
## 1                                      Engine 4/Ladder 15       42 South Street
## 2                                     Engine 10/Ladder 10    124 Liberty Street
## 3                                                Engine 6     49 Beekman Street
## 4 Engine 7/Ladder 1/Battalion 1/Manhattan Borough Command  100-104 Duane Street
## 5                                                Ladder 8 14 North Moore Street
## 6                                       Engine 9/Ladder 6       75 Canal Street
##     Borough Postcode Latitude Longitude Community.Board Community.Council
## 1 Manhattan    10005 40.70347 -74.00754               1                 1
## 2 Manhattan    10006 40.71007 -74.01252               1                 1
## 3 Manhattan    10038 40.71005 -74.00525               1                 1
## 4 Manhattan    10007 40.71546 -74.00594               1                 1
## 5 Manhattan    10013 40.71976 -74.00668               1                 1
## 6 Manhattan    10002 40.71521 -73.99290               3                 1
##   Census.Tract     BIN        BBL
## 1            7 1000867 1000350001
## 2           13 1075700 1000520022
## 3         1501 1001287 1000930030
## 4           33 1001647 1001500025
## 5           33 1002150 1001890035
## 6           16 1003898 1003000030
##                                                                           NTA
## 1 Battery Park City-Lower Manhattan                                          
## 2 Battery Park City-Lower Manhattan                                          
## 3 Battery Park City-Lower Manhattan                                          
## 4 SoHo-TriBeCa-Civic Center-Little Italy                                     
## 5 SoHo-TriBeCa-Civic Center-Little Italy                                     
## 6 Chinatown

summary(firefighter_stations)

##  FacilityName       FacilityAddress      Borough             Postcode    
##  Length:218         Length:218         Length:218         Min.   :10001  
##  Class :character   Class :character   Class :character   1st Qu.:10304  
##  Mode  :character   Mode  :character   Mode  :character   Median :11103  
##                                                           Mean   :10784  
##                                                           3rd Qu.:11231  
##                                                           Max.   :11695  
##                                                           NA's   :5      
##     Latitude       Longitude      Community.Board  Community.Council
##  Min.   :40.51   Min.   :-74.24   Min.   : 1.000   Min.   : 1.00    
##  1st Qu.:40.66   1st Qu.:-73.99   1st Qu.: 3.000   1st Qu.:12.00    
##  Median :40.72   Median :-73.94   Median : 6.000   Median :27.00    
##  Mean   :40.72   Mean   :-73.94   Mean   : 7.075   Mean   :25.63    
##  3rd Qu.:40.77   3rd Qu.:-73.89   3rd Qu.:11.000   3rd Qu.:38.00    
##  Max.   :40.89   Max.   :-73.72   Max.   :84.000   Max.   :51.00    
##  NA's   :5       NA's   :5        NA's   :5        NA's   :5        
##   Census.Tract         BIN               BBL                NTA           
##  Min.   :     1   Min.   :1000867   Min.   :1.000e+09   Length:218        
##  1st Qu.:   129   1st Qu.:2003268   1st Qu.:2.025e+09   Class :character  
##  Median :   275   Median :3064786   Median :3.025e+09   Mode  :character  
##  Mean   :  5950   Mean   :2900421   Mean   :2.850e+09                     
##  3rd Qu.:   800   3rd Qu.:4090228   3rd Qu.:4.033e+09                     
##  Max.   :157902   Max.   :5154879   Max.   :5.080e+09                     
##  NA's   :5        NA's   :5         NA's   :5

We now start with the firefighter stations dataset. By first making a copy of the fire_data_new and setting the borough from the firefighter_stations dataset to factor in order to be easily merged with the copied fire_data dataset.

# make a copy of the fire_data
fire_data_for_ffs <- fire_data_new

fire_data_for_ffs <- fire_data_for_ffs %>% rename(borough = inc_borough)

firefighter_stations$Borough <- as.factor(firefighter_stations$Borough)
firefighter_stations <- firefighter_stations %>% rename(borough = Borough)

# remove the na values from firefighter_stations
firefighter_stations <- na.omit(firefighter_stations)

Now we want to get the number of firefighter station for each borough.

stations_borough <- firefighter_stations %>%
                    group_by(borough) %>%
                    summarise(number_of_stations = n())

Now we want to get a summary of the incident count, the number of station and the incident per station of each borough in order to have a general view of the New York City situation.

This step is done twice we have to group both for borough and then for congressional district.

count_inc_brough <- fire_data_for_ffs %>% group_by(borough) %>% summarise(incident_count = n())
count_inc_cdist <- fire_data_for_ffs %>% group_by(cong_dist) %>% summarise(incident_count = n())

stations_borough$incident_per_station <- round(count_inc_brough$incident_count / stations_borough$number_of_stations, digits = 3)

count_inc_brough <- merge(count_inc_brough, stations_borough, by="borough")

count_inc_brough

##         borough incident_count number_of_stations incident_per_station
## 1         Bronx           6406                 34              188.412
## 2      Brooklyn           9280                 64              145.000
## 3     Manhattan           7294                 47              155.191
## 4        Queens           6188                 48              128.917
## 5 Staten Island           1560                 20               78.000

Now we convert the firefighter_station data frame into a Spartial Data Frame to contains the geometry points.

firefighter_stations_sdf <- st_as_sf(firefighter_stations, coords = c("Longitude", "Latitude"), crs = 4326)
head(firefighter_stations_sdf)

## Simple feature collection with 6 features and 10 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -74.01252 ymin: 40.70347 xmax: -73.9929 ymax: 40.71976
## Geodetic CRS:  WGS 84
##                                              FacilityName       FacilityAddress
## 1                                      Engine 4/Ladder 15       42 South Street
## 2                                     Engine 10/Ladder 10    124 Liberty Street
## 3                                                Engine 6     49 Beekman Street
## 4 Engine 7/Ladder 1/Battalion 1/Manhattan Borough Command  100-104 Duane Street
## 5                                                Ladder 8 14 North Moore Street
## 6                                       Engine 9/Ladder 6       75 Canal Street
##     borough Postcode Community.Board Community.Council Census.Tract     BIN
## 1 Manhattan    10005               1                 1            7 1000867
## 2 Manhattan    10006               1                 1           13 1075700
## 3 Manhattan    10038               1                 1         1501 1001287
## 4 Manhattan    10007               1                 1           33 1001647
## 5 Manhattan    10013               1                 1           33 1002150
## 6 Manhattan    10002               3                 1           16 1003898
##          BBL
## 1 1000350001
## 2 1000520022
## 3 1000930030
## 4 1001500025
## 5 1001890035
## 6 1003000030
##                                                                           NTA
## 1 Battery Park City-Lower Manhattan                                          
## 2 Battery Park City-Lower Manhattan                                          
## 3 Battery Park City-Lower Manhattan                                          
## 4 SoHo-TriBeCa-Civic Center-Little Italy                                     
## 5 SoHo-TriBeCa-Civic Center-Little Italy                                     
## 6 Chinatown                                                                  
##                     geometry
## 1 POINT (-74.00754 40.70347)
## 2 POINT (-74.01252 40.71007)
## 3 POINT (-74.00525 40.71005)
## 4 POINT (-74.00594 40.71546)
## 5 POINT (-74.00668 40.71976)
## 6  POINT (-73.9929 40.71521)

5.5.4.1 Downloand of the geojson files

At this point we download the .geojson files that contain all the geometry of each borough in order to have a cool maps visualization of NYC.

Here we load the NYC_BoroughBoundaries.geojson.

geoj_nyc_borough <- geojson_read("datasets/NYC_BoroughBoundaries.geojson",  what = "sp")
geoj_nyc_borough <- setNames(geoj_nyc_borough, c("borough_code", "borough", "shape_area", "shape_leng"))
geoj_nyc_borough$borough <- as.factor(geoj_nyc_borough$borough)
geoj_nyc_borough$borough_code <- NULL
head(geoj_nyc_borough)

##         borough    shape_area    shape_leng
## 1 Staten Island 1623620725.05  325917.35395
## 2     Manhattan 636520502.758 357713.308162
## 3         Bronx  1187174772.5 463180.579449
## 4      Brooklyn 1934138215.76 728146.574928
## 5        Queens 3041418506.64 888199.731385

And here the Congressiona_Districts.geojson.

geoj_nyc_cdist <- geojson_read("datasets/Congressional_Districts.geojson",  what = "sp")
geoj_nyc_cdist$cong_dist <- as.factor(geoj_nyc_cdist$cong_dist)
head(geoj_nyc_cdist)

##   cong_dist    shape_area    shape_leng
## 1        16 53350456.3831  38220.195314
## 2        11 1810287145.51 411602.819728
## 3         5 1313441026.63 647233.193924
## 4         6 723356617.906 198557.173232
## 5         8 736292261.799 575320.090507
## 6         9 422693976.037 115321.546362

And now we merge geoj_nyc_borough with count_inc_brough maintaining the Spartial Data Frame type.

geoj_nyc_borough@data = data.frame(geoj_nyc_borough@data, count_inc_brough[match(geoj_nyc_borough@data$borough, count_inc_brough$borough),])
geoj_nyc_borough@data$borough.1 <- NULL # remove the added column

Here we are making the same step as before but this this with the geoj_nyc_cdist merging via cong_dist, again maintaining the Spartial Data Frame type.

geoj_nyc_cdist@data = data.frame(geoj_nyc_cdist@data, count_inc_cdist[match(geoj_nyc_cdist@data$cong_dist, count_inc_cdist$cong_dist),])
geoj_nyc_cdist@data$cong_dist.1 <- NULL # remove the added column

And finally we can plot the interactive maps using the mapview function.

First for Incident Borough:

mapview(list(firefighter_stations_sdf, geoj_nyc_borough),
        zcol = list(NULL, "incident_count"),
        legend = list(FALSE, TRUE),
        homebutton = list(FALSE, TRUE),
        layer.name = list(NULL, "Incidents Count"),
        popup = list(popupTable(firefighter_stations_sdf), popupTable(geoj_nyc_borough)),
        alpha.regions = 0.5, aplha = 1)

And then for Congressional District:

mapview(geoj_nyc_cdist,
        zcol = "incident_count",
        legend = TRUE,
        popup = popupTable(geoj_nyc_cdist),
        homebutton = TRUE,
        layer.name = "Incidents Count",
        alpha.regions = 0.5, aplha = 1)

6 Before Doing some Models

As suggested by the professor we have opted to linear regression using as response:

1- inc_resp_min_qy 2- emergency_min_qy

Initially we were thinking to solve a multi-classification / binary classification problem for the inc_class_group, however we were considering all the time difference predictors that are a future information w.r.t. the inc_class_group in prediction time, so it doesn’t make much sense to use them, and it is possible also that they will result in super predictors. That’s way we decided to grab the professor suggestion.

For both analysis we transform the relative response in log scale in order to simulate the behaviour of the Log-Norm distribution. Of course we have to verify that the linearty assumption are meet.

So first things first let’s check if there are some observations that have at least one of the two possible responses equal to zero, if so we have to remove then in order to apply next the log transformation.

summary(fire_data_new %>% select(inc_resp_min_qy, emergency_min_qy))

##  inc_resp_min_qy  emergency_min_qy
##  Min.   : 0.350   Min.   :  0.00  
##  1st Qu.: 4.383   1st Qu.:  7.15  
##  Median : 5.467   Median : 11.93  
##  Mean   : 5.949   Mean   : 17.08  
##  3rd Qu.: 6.850   3rd Qu.: 19.58  
##  Max.   :58.917   Max.   :596.38

Remove them.

fire_data_new <- fire_data_new %>% filter(emergency_min_qy != 0)

Then we have to check the presence of correlation in the continuous predictors and if so deleting one or more of them. Thus here we are doing an initial chec of multicollinearity problems only in the numerical variable before once we create our for the future models. In order to do so compute the square of the correlation matrix.

round(cor(fire_data_new %>% dplyr::select(where(is.numeric)))^2, digits=3)

##                       inc_resp_min_qy engines_assigned ladders_assigned
## inc_resp_min_qy                 1.000            0.064            0.015
## engines_assigned                0.064            1.000            0.317
## ladders_assigned                0.015            0.317            1.000
## others_units_assigned           0.023            0.294            0.325
## emergency_min_qy                0.000            0.046            0.017
## total_assigned_unit             0.045            0.719            0.693
##                       others_units_assigned emergency_min_qy
## inc_resp_min_qy                       0.023            0.000
## engines_assigned                      0.294            0.046
## ladders_assigned                      0.325            0.017
## others_units_assigned                 1.000            0.121
## emergency_min_qy                      0.121            1.000
## total_assigned_unit                   0.704            0.077
##                       total_assigned_unit
## inc_resp_min_qy                     0.045
## engines_assigned                    0.719
## ladders_assigned                    0.693
## others_units_assigned               0.704
## emergency_min_qy                    0.077
## total_assigned_unit                 1.000

As we can see total_assigned_unit is heavily correlated to the other counts since it is the sum of those, thus deceide to remove the sum of units. Continuing we note also that lot’s of time difference are correlated to each other, whoever it is obvious since some of them include other smaller difference, these measures will be managed soon once we deal with the two type of analysis.

fire_data_new <- fire_data_new %>% select(-c(total_assigned_unit))

Next before creating any model have to split the cleaned dataset into train and test, with 80% of the whole dataset for the train set and the remaining 20% for the test set. We want to mention that it would be better to perform cross validation using the entire data but for stake of simplicity and time we decide to opt for the classic train-test split.

set.seed(43)
split <- sample.split(fire_data_new, SplitRatio = 0.8)

# Create training and testing sets
fire_data.train <- subset(fire_data_new, split == TRUE)
fire_data.test <- subset(fire_data_new, split == FALSE)

rownames(fire_data.train) <- NULL
rownames(fire_data.test) <- NULL

dim(fire_data.train)

## [1] 24580    15

dim(fire_data.test)

## [1] 6146   15

Save the cleaned train and test dataframe for the next part of the analysis.

write.csv(fire_data.train, "datasets/fire_data_clean.train.csv", row.names=FALSE)
write.csv(fire_data.test, "datasets/fire_data_clean.test.csv", row.names=FALSE)

Please go to the Part 2 - Linear Regression to continue the analysis.